Search results for: logistic regression with random effects

Authors: Miral Selim, May Haggag, Ibrahim Abotaleb

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The aging of transportation infrastructure presents significant challenges, particularly concerning the monitoring and maintenance of bridges. This study investigates the application of Random Forest algorithms for predictive modeling of bridge conditions, utilizing data from the US National Bridge Inventory (NBI). The research is significant as it aims to improve bridge management through data-driven insights that can enhance maintenance strategies and contribute to overall safety. Random Forest is chosen for its robustness, ability to handle complex, non-linear relationships among variables, and its effectiveness in feature importance evaluation. The study begins with comprehensive data collection and cleaning, followed by the identification of key variables influencing bridge condition ratings, including age, construction materials, environmental factors, and maintenance history. Random Forest is utilized to examine the relationships between these variables and the predicted bridge conditions. The dataset is divided into training and testing subsets to evaluate the model's performance. The findings demonstrate that the Random Forest model effectively enhances the understanding of factors affecting bridge conditions. By identifying bridges at greater risk of deterioration, the model facilitates proactive maintenance strategies, which can help avoid costly repairs and minimize service disruptions. Additionally, this research underscores the value of data-driven decision-making, enabling better resource allocation to prioritize maintenance efforts where they are most necessary. In summary, this study highlights the efficiency and applicability of Random Forest in predictive modeling for bridge management. Ultimately, these findings pave the way for more resilient and proactive management of bridge systems, ensuring their longevity and reliability for future use.

Keywords: data analysis, random forest, predictive modeling, bridge management

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Authors: Abdullah A. AlShaher

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In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2^nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

Keywords: character recognition, regression curves, handwritten Arabic letters, expectation maximization algorithm

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Authors: Khandoker Akib Mohammad

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While analyzing time-to-event data, it is possible that a certain fraction of subjects will never experience the event of interest, and they are said to be cured. When this feature of survival models is taken into account, the models are commonly referred to as cure models. In the presence of covariates, the conditional survival function of the population can be modelled by using the cure model, which depends on the probability of being uncured (incidence) and the conditional survival function of the uncured subjects (latency), and a combination of logistic regression and Cox proportional hazards (PH) regression is used to model the incidence and latency respectively. In this paper, we have shown the asymptotic normality of the profile likelihood estimator via asymptotic expansion of the profile likelihood and obtain the explicit form of the variance estimator with an implicit function in the profile likelihood. We have also shown the efficient score function based on projection theory and the profile likelihood score function are equal. Our contribution in this paper is that we have expressed the efficient information matrix as the variance of the profile likelihood score function. A simulation study suggests that the estimated standard errors from bootstrap samples (SMCURE package) and the profile likelihood score function (our approach) are providing similar and comparable results. The numerical result of our proposed method is also shown by using the melanoma data from SMCURE R-package, and we compare the results with the output obtained from the SMCURE package.

Keywords: Cox PH model, cure model, efficient score function, EM algorithm, implicit function, profile likelihood

Procedia PDF Downloads 144

Authors: Samira Cherid, Samir Bentata, F. Zahira Meghoufel, Sabria Terkhi, Yamina Sefir, Fatima Bendahma, Bouabdellah Bouadjemi, Ali Z. Itouni

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In this work, we examined the propagation of light in one-dimensional systems is examined by means of the random dimer model. The introduction of defect elements, randomly in the studied system, breaks down the Anderson localization and provides a set of propagating delocalized modes at the corresponding conventional dimer resonances. However, tuning suitably the defect dimer resonance on the host ones (or vice versa), the transmission magnitudes can be enhanced providing the optimized ballistic transmission regime as an average response. Hence, ballistic optical filters can be conceived at desired wavelengths.

Keywords: photonic crystals, random dimer model, ballistic resonance, localization and transmission

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Authors: Misganaw Chere Siferih

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This study assessed the impact of crossbreed dairy cows on household income and food security. The study area is found in Dejen Woreda, East Gojam Zone, and Amhara region of Ethiopia. Random sampling technique was used to obtain a sample of 80 crossbreed dairy cow owners and 176 indigenous dairy cow owners. The study employed food consumption score analytical framework to measure food security status of the household. No Statistical significant mean difference is found between crossbreed owners and indigenous owners. Logistic regression was employed to investigate crossbreed dairy cow adoption determinants , the result indicates that gender, education, labor number, land size cultivated, dairy cooperatives membership, net income and food security status of the household are statistically significant independent variables, which explained the binary dependent variable, crossbreed dairy cow adoption. Propensity score matching (PSM) was employed to analyze the impact of crossbreed dairy cow owners on farmers’ income and food security. The average net income of crossbreed dairy cow owners was found to be significantly higher than indigenous dairy cow owners. Estimates of average treatment effect of the treated (ATT) indicated that crossbreed dairy cow is able to impact households’ net income by 42%, 38.5%, 30.8% and 44.5% higher in kernel, radius, nearest neighborhood and stratification matching algorithms respectively as compared to indigenous dairy cow owners. However, estimates of average treatment of the treated (ATT) suggest that being an owner of crossbreed dairy cow is not able to affect food security significantly. Thus, crossbreed dairy cow enables farmers to increase income but not their food security in the study area. Finally, the study recommended establishing dairy cooperatives and advice farmers to become a member of them, attention to promoting the impact of crossbreed dairy cows and promotion of nutrition focus projects.

Keywords: crossbreed dairy cow, net income, food security, propensity score matching

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Authors: P. O. Fatoki, R. S. Olaleye, B. O. Adeniji

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The study investigated farmers’ perception of the effects of climate change on rice production in Nasarawa State, Nigeria. Multi-stage sampling technique was used in selecting a total of 248 rice farmers from the study area. Data for the study were collected through the use of interview schedule. The data were analysed using both descriptive and inferential statistics. Results showed that majority (71.8%) of the respondents were married and the mean age of the respondents was 44.54 years. The results also showed that most adapted strategies for mitigating the effects of climate change on rice production were change of planting and harvesting date (67.7%), movement to another site (63.7%) and increased or reduced land size (58.5%). Relationship between the roles of extension agents in mitigating climate change effects on rice production and farmers’ perception were significant as revealed Chi-Square analysis from the study ; Dissemination of information ( = 2.16, P < 0.05) and use of demonstration methods ( = 2.15, P < 0.05). Poisson regression analysis revealed that educational status, farm size, experience and yield had significant relationship with the perception of the effects of climate change at 0.01 significance level while household size was as well significant at 0.05. It is recommended that some of the adaptive strategies and practices for mitigating the effects of climate change in rice production should be improved, while the extension outfits should be strengthened to ensure adequate dissemination of relevant information on climate change with a view to mitigate its effects on rice production.

Keywords: perception, rice farmers, climate change, mitigation, adaptive strategies

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Authors: Dalia Rimawi, Walid Salameh, Amal Al-Omari, Hadeel AbdelKhaleq

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Predication of Survival time of patients with cancer, is a core factor that influences oncologist decisions in different aspects; such as offered treatment plans, patients’ quality of life and medications development. For a long time proportional hazards Cox regression (ph. Cox) was and still the most well-known statistical method to predict survival outcome. But due to the revolution of data sciences; new predication models were employed and proved to be more flexible and provided higher accuracy in that type of studies. Artificial neural network is one of those models that is suitable to handle time to event predication. In this study we aim to compare ph Cox regression with artificial neural network method according to data handling and Accuracy of each model.

Keywords: Cox regression, neural networks, survival, cancer.

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Authors: Al Omari Mohammed Ahmed

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This paper focuses on Maximum Likelihood Estimator with Covariate. Covariates are incorporated into the Weibull model. Under this regression model with regards to maximum likelihood estimator, the parameters of the covariate, shape parameter, survival function and hazard rate of the Weibull regression distribution with right censored data are estimated. The mean square error (MSE) and absolute bias are used to compare the performance of Weibull regression distribution. For the simulation comparison, the study used various sample sizes and several specific values of the Weibull shape parameter.

Keywords: weibull regression distribution, maximum likelihood estimator, survival function, hazard rate, right censoring

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Authors: Olifan Degebas Olkeba

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Background: Males participate in family planning by utilizing the method, having discussions, approving decisions, and supporting their partners and other family members. One of the reasons Ethiopia has a low rate of FP use is the poor participation of men in family planning. So, the finding of the study could help married men and other stakeholders to alleviate the problems related to low involvement. Objective: To assess males’ involvement in family planning use and associated factors among married men in the pastoralist community of Yabelo district, Borena, Oromia, Ethiopia, 2024. Methods: Cross sectional study design supplemented by qualitative and multistage sampling method for quantitative one and purposive sampling method for qualitative was done. The interviewer administered questionnaires from 531 samples for quantitative and from 14 key informants for qualitative were taken. Quantitative data were entered using Epi Info version 7.2.2.6 and analyzed using SPSS version 24. Bivariate associations between dependent and independent variables were examined. Multi variable logistic regression analysis was done to identify factors significantly associated with male involvement. Qualitative data was analyzed using open code 4.03. The study was conducted from January 1-February 29, 2024. Results: A total of 531 respondents participated. The mean age of the study participant was 28 ±2.1 (SD). The prevalence of male involvement in FP use among married males in Yabelo district was 9.6 (AOR= 9.6, 95% CI: 7.14-12.15). Age above 40 years (AOR=0.18, 95% CI: 0.05-0.6 p=0.009), educational status unable read and write (AOR=9.4, 95% CI:3.5-25.4 p=0.001), read and write only (AOR=7.1, 95% CI:2.4-21.4 p=0.001), knowledge on side effects of FP (AOR=2.35, 95% CI: 1.09-5.06 p=0.029) were factors associated with male involvement in FP use. A total of 14 key informants participated in the interview of qualitative part and culturally perceived FP issues, lack of awareness and desire of more children were among the reasons for low involvement in FP use. Conclusion: The finding of the study showed that the magnitude of male involvement in family planning use was low. Age (>40), educational status (read and write only) and fear of side effects were factors associated with low husband involvement in FP use. Therefore, family planning programs need to target men at all levels of the service.

Keywords: family planning, male involvement, married men, Yabelo district

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Authors: Angelika Kris D. Dalangin, Cesar B. Quicoy

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Rice farming is a major source of livelihood and employment in the Philippines, but it requires a substantial amount of capital. Capital may come from income (farm, non-farm, and off-farm), savings and credit. However, rice farmers suffer from lack of capital due to high costs of inputs and low productivity. Capital insufficiency, coupled with low productivity, hindered them to meet their basic household and production needs. Hence, they resorted to borrowing money, mostly from informal lenders who charge very high interest rates. As another source of capital, savings can help rice farmers meet their basic needs for both the household and the farm. However, information is inadequate whether the farmers save or not, as well as, why they do not depend on savings to augment their lack of capital. Thus, it is worth analyzing how rice farmers saved. The study revealed, using the actual savings which is the difference between the household income and expenditure, that about three-fourths (72%) of the total number of farmers interviewed are savers. However, when they were asked whether they are savers or not, more than half of them considered themselves as non-savers. This gap shows that there are many farmers who think that they do not have savings at all; hence they continue to borrow money and do not depend on savings to augment their lack of capital. The study also identified the forms of savings, saving motives, and savings utilization among rice farmers. Results revealed that, for the past 12 months, most of the farmers saved cash at home for liquidity purposes while others deposited cash in banks and/or saved their money in the form of livestock. Among the most important reasons of farmers for saving are for daily household expenses, for building a house, for emergency purposes, for retirement, and for their next production. Furthermore, the study assessed the factors affecting the rice farmers’ savings behaviour using logistic regression. Results showed that the factors found to be significant were presence of non-farm income, per capita net farm income, and per capita household expense. The presence of non-farm income and per capita net farm income positively affects the farmers’ savings behaviour. On the other hand, per capita household expenses have negative effect. The effect, however, of per capita net farm income and household expenses is very negligible because of the very small chance that the farmer is a saver. Generally, income and expenditure were proved to be significant factors that affect the savings behaviour of the rice farmers. However, most farmers could not save regularly due to low farm income and high household and farm expenditures. Thus, it is highly recommended that government should develop programs or implement policies that will create more jobs for the farmers and their family members. In addition, programs and policies should be implemented to increase farm productivity and income.

Keywords: agricultural economics, agricultural finance, binary logistic regression, logit, Philippines, Quezon, rice farmers, savings, savings behaviour

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Authors: Mochamad Beta Auditama, Yusuf Kurniawan

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A smartphone or tablet require a strong randomness to establish secure encrypted communication, encrypt files, etc. Therefore, random number generation is one of the main keys to provide secrecy. Android devices are equipped with hardware-based sensors, such as accelerometer, gyroscope, etc. Each of these sensors provides a stochastic process which has a potential to be used as an extra randomness source, in addition to /dev/random and /dev/urandom pseudorandom number generators. Android sensors can provide randomness automatically. To obtain randomness from Android sensors, each one of Android sensors shall be used to construct an entropy source. After all entropy sources are constructed, output from these entropy sources are combined to provide more entropy. Then, a deterministic process is used to produces a sequence of random bits from the combined output. All of these processes are done in accordance with NIST SP 800-22 and the series of NIST SP 800-90. The operation conditions are done 1) on Android user-space, and 2) the Android device is placed motionless on a desk.

Keywords: Android hardware-based sensor, deterministic process, entropy source, random number generation/generators

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Authors: Navab Karimi, Tohid Alizadeh

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An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

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Authors: Wudneh Asegedech Ayele

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Background: Dietary diversity is strongly related with non-communicable disease (NCDs). Diet plays a key role as a risk factor for hypertension. Diets rich in fruits, vegetables, and low-fat dairy products that include whole grains, poultry, fish, and nuts, that contain only small amounts of red meat, sweets, and sugar-containing beverages, and that contain decreased amounts of total and saturated fat and cholesterol have been found to have a protective effect against hypertension. Methods: hospital based Cross-sectional study design was employed from June 1-June 25, 2021. Sampling technique was Systematic random sampling and data were collected using an interview method. Data were entered into Epi Data version 3.1 and exported to SPSS version 25 for processed and analysis respectively. Descriptive statistics were used to summarize data. Bivariate and multivariate logistic regression will employed to determine dietary diversity among hypertension patients. Results: Adequate dietary diversity score were 96 (24.68%). Most of them cereal, white roots and tubers, dark green leafy vegetables, Vitamin A rich fruits ,meat, egg and coffee or tea more intakes. Hypertensive patients who didn’t consume cereals four times less likely adequate dietary diversity than who consumed cereals [AOR= 4.083, 95%: CI (2.096 -7.352)]. Hypertensive patients who didn’t consume white roots and tubers 14 times less likely adequate dietary diversity than who consumed white roots and tubers [AOR= 13.733, 95% CI: (5.388-34.946)]. Conclusion and recommendation the study showed one of fourth part reported adequate dietary diversity score. Cereals, fruits, vegetables and milk and milk products were statistically associated with dietary diversity practice. Health education about dietary modifications and behavioral change to dietary diversity

Keywords: dietary diversity practice and associated facrors among hypertension patients at tirunesh beijing hospital, hypertension, dietary, diversity and tirunesh beijing hospital, associated facrors among hypertension patient, at tirunesh beijing hospita

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Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

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Authors: X. Li, J. Sundquist, C. Sjöstedt, M. Winkleby, K. S. Kendler, K. Sundquist

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Aims: This study examines the association between the incidence of drug abuse (DA) and linking (communal) social capital, a theoretical concept describing the amount of trust between individuals and societal institutions. Methods: We present results from an 8-year population-based cohort study that followed all residents in Sweden, aged 15-44, from 2003 through 2010, for a total of 1,700,896 men and 1,642,798 women. Social capital was conceptualized as the proportion of people in a geographically defined neighborhood who voted in local government elections. Multilevel logistic regression was used to estimate odds ratios (ORs) and between-neighborhood variance. Results: We found robust associations between linking social capital (scored as a three level variable) and DA in men and women. For men, the OR for DA in the crude model was 2.11 [95% confidence interval (CI) 2.02-2.21] for those living in areas with the lowest vs. highest level of social capital. After accounting for neighborhood-level deprivation, the OR fell to 1.59 (1.51-1-68), indicating that neighborhood deprivation lies in the pathway between linking social capital and DA. The ORs remained significant after accounting for age, sex, family income, marital status, country of birth, education level, and region of residence, and after further accounting for comorbidities and family history of comorbidities and family history of DA. For women, the OR decreased from 2.15 (2.03-2.27) in the crude model to 1.31 (1.22-1.40) in the final model, adjusted for multiple neighborhood-level and individual-level variables. Conclusions: Our study suggests that low linking social capital may have important independent effects on DA.

Keywords: drug abuse, social linking capital, environment, family

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Authors: Nicola Rubino

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This paper presents an analysis of a group of commodity exporting countries' nominal exchange rate movements in relationship to the US dollar. Using a series of Unrestricted Self-exciting Threshold Autoregressive models (SETAR), we model and evaluate sixteen national CPI price differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is evaluated through calculation of mean absolute error measures on the basis of two-hundred and fifty-three months rolling window forecasts and extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive non linear autoregressive model (AAR) and a simple linear Neural Network model (NNET). Our preliminary results confirm presence of some form of TAR non linearity in the majority of the countries analyzed, with a relatively higher goodness of fit, with respect to the linear AR(1) benchmark, in five countries out of sixteen considered. Although no model appears to statistically prevail over the other, our final out-of-sample forecast exercise shows that SETAR models tend to have quite poor relative forecasting performance, especially when compared to alternative non-linear specifications. Finally, by analyzing the implied half-lives of the > coefficients, our results confirms the presence, in the spirit of arbitrage band adjustment, of band convergence with an inner unit root behaviour in five of the sixteen countries analyzed.

Keywords: transition regression model, real exchange rate, nonlinearities, price differentials, PPP, commodity points

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Authors: Morteza Raei Dehaghi, Seyed Mohammad Mirhashemi

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The current study explored the effect of economic development, financial development and institutional quality on environmental destruction in upper-middle income countries during the time period of 1999-2011. The dependent variable is logarithm of carbon dioxide emissions that can be considered as an index for destruction or quality of the environment given to its effects on the environment. Financial development and institutional development variables as well as some control variables were considered. In order to study cross-sectional correlation among the countries under study, Pesaran and Friz test was used. Since the results of both tests show cross-sectional correlation in the countries under study, seemingly unrelated regression method was utilized for model estimation. The results disclosed that Kuznets’ environmental curve hypothesis is confirmed in upper-middle income countries and also, financial development and institutional quality have a significant effect on environmental quality. The results of this study can be considered by policy makers in countries with different income groups to have access to a growth accompanied by improved environmental quality.

Keywords: economic development, environmental destruction, financial development, institutional development, seemingly unrelated regression

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Authors: M. Illias Kanchan

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The death of a woman during pregnancy and childbirth is not only a health issue, but also a matter of social injustice. This study makes an attempt to explore the association between maternal death and associated factors in West Bengal using the approaches of facility-based and community-based maternal death review. Bivariate and binary logistic regression analysis have been performed to understand the causes and circumstances of maternal deaths in West Bengal. Delay in seeking care was the major contributor in maternal deaths, near about one-third women died due to this factor. The most common cause of maternal death is found to be hypertensive disorders of pregnancy or eclampsia. We believe that these deaths can be averted by reducing hypertensive disorders of pregnancy or eclampsia.

Keywords: maternal death, facility-based, community-based, review, west Bengal, eclampsia

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Authors: Yongrong Liu, Xin Tang

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Background: Persistent atrial fibrillation is a common arrhythmia closely related to heart failure. Heart rate control is an essential strategy for treating persistent atrial fibrillation. Still, the understanding of the relationship between different heart rate control levels and the risk of heart failure rehospitalization is limited. Objective: The objective of the study is to determine the relationship between different levels of heart rate control in patients with persistent atrial fibrillation and the risk of readmission for heart failure. Methods: We conducted a retrospective dual-centre cohort study, collecting data from patients with persistent atrial fibrillation who received outpatient treatment at two tertiary hospitals in central and western China from March 2019 to March 2020. The collected data included age, gender, body mass index (BMI), medical history, and hospitalization frequency due to heart failure. Patients were divided into three groups based on their heart rate control levels: Group I with a resting heart rate of less than 80 beats per minute, Group II with a resting heart rate between 80 and 100 beats per minute, and Group III with a resting heart rate greater than 100 beats per minute. The readmission rates due to heart failure within one year after discharge were statistically analyzed using propensity score matching in a 1:1 ratio. Differences in readmission rates among the different groups were compared using one-way ANOVA. The impact of varying levels of heart rate control on the risk of readmission for heart failure was assessed using the Cox proportional hazards model. Binary logistic regression analysis was employed to control for potential confounding factors. Results: We enrolled a total of 1136 patients with persistent atrial fibrillation. The results of the one-way ANOVA showed that there were differences in readmission rates among groups exposed to different levels of heart rate control. The readmission rates due to heart failure for each group were as follows: Group I (n=432): 31 (7.17%); Group II (n=387): 11.11%; Group III (n=317): 90 (28.50%) (F=54.3, P<0.001). After performing 1:1 propensity score matching for the different groups, 223 pairs were obtained. Analysis using the Cox proportional hazards model showed that compared to Group I, the risk of readmission for Group II was 1.372 (95% CI: 1.125-1.682, P<0.001), and for Group III was 2.053 (95% CI: 1.006-5.437, P<0.001). Furthermore, binary logistic regression analysis, including variables such as digoxin, hypertension, smoking, coronary heart disease, and chronic obstructive pulmonary disease as independent variables, revealed that coronary heart disease and COPD also had a significant impact on readmission due to heart failure (p<0.001). Conclusion: The correlation between the heart rate control level of patients with persistent atrial fibrillation and the risk of heart failure rehospitalization is positive. Reasonable heart rate control may significantly reduce the risk of heart failure rehospitalization.

Keywords: heart rate control levels, heart failure rehospitalization, persistent atrial fibrillation, retrospective cohort study

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Authors: Maha Zouaoui Khalifa, Hakim Ben Othman, Hussaney Khaled

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Prior empirical studies have investigated the economic consequences of accounting conservatism by examining its impact on the cost of equity capital (COEC). However, findings are not conclusive. We assume that inconsistent results of such association may be attributed to the regression models used in data analysis. To address this issue, we re-examine the effect of different dimension of accounting conservatism: unconditional conservatism (U_CONS) and conditional conservatism (C_CONS) on the COEC for a sample of listed firms from Middle Eastern and North Africa (MENA) countries, applying quantile regression (QR) approach developed by Koenker and Basset (1978). While classical ordinary least square (OLS) method is widely used in empirical accounting research, however it may produce inefficient and bias estimates in the case of departures from normality or long tail error distribution. QR method is more powerful than OLS to handle this kind of problem. It allows the coefficient on the independent variables to shift across the distribution of the dependent variable whereas OLS method only estimates the conditional mean effects of a response variable. We find as predicted that U_CONS has a significant positive effect on the COEC however, C_CONS has a negative impact. Findings suggest also that the effect of the two dimensions of accounting conservatism differs considerably across COEC quantiles. Comparing results from QR method with those of OLS, this study throws more lights on the association between accounting conservatism and COEC.

Keywords: unconditional conservatism, conditional conservatism, cost of equity capital, OLS, quantile regression, emerging markets, MENA countries

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Authors: A. Sathiya Susuman, N. Wilson

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Background: The sexual behaviour of youths is believed to play an important role in the spread of sexually transmitted infections (STIs). Method: The data from the Malawi Demographic and Health Survey 2010 and a sample of 16,217 youth’s age 15 to 24 years (with each household 27.2% female and 72.8% male) was the basis for analysis. Bivariate and logistic regression analysis was performed. Results: The result shows married youth were not interested in condom use (94.2%, p<0.05). Those who were living together were 69 times (OR=1.69, 95% CI, 1.26–2.26) more likely to be involved in early sexual activity compared to those who were not living together. Conclusion: This scientific paper will help other researchers, policy makers, and planners to create strategies to encourage these youths to make use of contraception.

Keywords: sexually transmitted infections (STIs), reproductive tract infections (RTIs), condom use, sexual partners, early sexual debut, youths

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Authors: Maria Reichenber Arcilla

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Objective: To determine the risk factors for maternal and neonatal complications associated with operative vaginal deliveries. Methods: A retrospective chart review of 435 patients who underwent operative vaginal deliveries was done. Patient profiles – age, parity, AOG, duration of labor – and outcomes – birthweight, maternal and neonatal complications - were tabulated and multivariable analysis and logistic regression were performed using SPSS® Statistics Base. Results and Conclusion: There was no significant difference in the incidence of maternal and neonatal complications between those that underwent vacuum and forceps extraction. Among the variables analysed, parity and duration of labor reached statistical significance. The odds of maternal complications were 3 times higher among nulliparous patients. Neonatal complications were seen in those whose labor lasted more than 9 hours.

Keywords: operative vaginal deliveries, maternal, neonatal, morbidity

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Authors: Shokrya Saleh A. Alshqaq, Abdullah Ali H. Ahmadini

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The Schwarz information criterion (SIC) is a popular tool for selecting the best variables in regression datasets. However, SIC is defined using an unbounded estimator, namely, the least-squares (LS), which is highly sensitive to outlying observations, especially bad leverage points. A method for robust variable selection based on SIC for linear regression models is thus needed. This study investigates the robustness properties of SIC by deriving its influence function and proposes a robust SIC based on the MM-estimation scale. The aim of this study is to produce a criterion that can effectively select accurate models in the presence of vertical outliers and high leverage points. The advantages of the proposed robust SIC is demonstrated through a simulation study and an analysis of a real dataset.

Keywords: influence function, robust variable selection, robust regression, Schwarz information criterion

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Authors: Joel Podgorski, Manouchehr Amini, Annette Johnson, Michael Berg

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One-third of the world’s population relies on groundwater for its drinking water. Natural geogenic arsenic and fluoride contaminate ~10% of wells. Prolonged exposure to high levels of arsenic can result in various internal cancers, while high levels of fluoride are responsible for the development of dental and crippling skeletal fluorosis. In poor urban and rural settings, the provision of drinking water free of geogenic contamination can be a major challenge. In order to efficiently apply limited resources in the testing of wells, water resource managers need to know where geogenically contaminated groundwater is likely to occur. The Groundwater Assessment Platform (GAP) fulfills this need by providing state-of-the-art global arsenic and fluoride contamination hazard maps as well as enabling users to create their own groundwater quality models. The global risk models were produced by logistic regression of arsenic and fluoride measurements using predictor variables of various soil, geological and climate parameters. The maps display the probability of encountering concentrations of arsenic or fluoride exceeding the World Health Organization’s (WHO) stipulated concentration limits of 10 µg/L or 1.5 mg/L, respectively. In addition to a reconsideration of the relevant geochemical settings, these second-generation maps represent a great improvement over the previous risk maps due to a significant increase in data quantity and resolution. For example, there is a 10-fold increase in the number of measured data points, and the resolution of predictor variables is generally 60 times greater. These same predictor variable datasets are available on the GAP platform for visualization as well as for use with a modeling tool. The latter requires that users upload their own concentration measurements and select the predictor variables that they wish to incorporate in their models. In addition, users can upload additional predictor variable datasets either as features or coverages. Such models can represent an improvement over the global models already supplied, since (a) users may be able to use their own, more detailed datasets of measured concentrations and (b) the various processes leading to arsenic and fluoride groundwater contamination can be isolated more effectively on a smaller scale, thereby resulting in a more accurate model. All maps, including user-created risk models, can be downloaded as PDFs. There is also the option to share data in a secure environment as well as the possibility to collaborate in a secure environment through the creation of communities. In summary, GAP provides users with the means to reliably and efficiently produce models specific to their region of interest by making available the latest datasets of predictor variables along with the necessary modeling infrastructure.

Keywords: arsenic, fluoride, groundwater contamination, logistic regression

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

Abstract:

Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

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Authors: Khalifa Maha, Ben Othman Hakim, Khaled Hussainey

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Prior empirical studies have investigated the economic consequences of accounting conservatism by examining its impact on the cost of equity capital (COEC). However, findings are not conclusive. We assume that inconsistent results of such association may be attributed to the regression models used in data analysis. To address this issue, we re-examine the effect of different dimension of accounting conservatism: unconditional conservatism (U_CONS) and conditional conservatism (C_CONS) on the COEC for a sample of listed firms from Middle Eastern and North Africa (MENA) countries, applying quantile regression (QR) approach developed by Koenker and Basset (1978). While classical ordinary least square (OLS) method is widely used in empirical accounting research, however it may produce inefficient and bias estimates in the case of departures from normality or long tail error distribution. QR method is more powerful than OLS to handle this kind of problem. It allows the coefficient on the independent variables to shift across the distribution of the dependent variable whereas OLS method only estimates the conditional mean effects of a response variable. We find as predicted that U_CONS has a significant positive effect on the COEC however, C_CONS has a negative impact. Findings suggest also that the effect of the two dimensions of accounting conservatism differs considerably across COEC quantiles. Comparing results from QR method with those of OLS, this study throws more lights on the association between accounting conservatism and COEC.

Keywords: unconditional conservatism, conditional conservatism, cost of equity capital, OLS, quantile regression, emerging markets, MENA countries

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Authors: Naushad Mamode Khan

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The Com-Poisson (CMP) model is one of the most popular discrete generalized linear models (GLMS) that handles both equi-, over- and under-dispersed data. In longitudinal context, an integer-valued autoregressive (INAR(1)) process that incorporates covariate specification has been developed to model longitudinal CMP counts. However, the joint likelihood CMP function is difficult to specify and thus restricts the likelihood based estimating methodology. The joint generalized quasilikelihood approach (GQL-I) was instead considered but is rather computationally intensive and may not even estimate the regression effects due to a complex and frequently ill conditioned covariance structure. This paper proposes a new GQL approach for estimating the regression parameters (GQLIII) that are based on a single score vector representation. The performance of GQL-III is compared with GQL-I and separate marginal GQLs (GQL-II) through some simulation experiments and is proved to yield equally efficient estimates as GQL-I and is far more computationally stable.

Keywords: longitudinal, com-Poisson, ill-conditioned, INAR(1), GLMS, GQL

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Authors: Arpine Maghakyan

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The purpose of this paper is to study the relationship between the level of industry digitalization and audit fees, especially, the relationship between Big 4 auditor fees and industry digitalization level. On the one hand, automation of business processes decreases internal control weakness and manual mistakes; increases work effectiveness and integrations. On the other hand, it may cause serious misstatements, high business risks or even bankruptcy, typically in early stages of automation. Incomplete automation can bring high audit risk especially if the auditor does not fully understand client’s business automation model. Higher audit risk consequently will cause higher audit fees. Higher audit fees for clients with high automation level are more highlighted in Big 4 auditor’s behavior. Using data of US firms from 2005-2015, we found that industry level digitalization is an interaction for the auditor quality on audit fees. Moreover, the choice of Big4 or non-Big4 is correlated with client’s industry digitalization level. Big4 client, which has higher digitalization level, pays more than one with low digitalization level. In addition, a high-digitalized firm that has Big 4 auditor pays higher audit fee than non-Big 4 client. We use audit fees and firm-specific variables from Audit Analytics and Compustat databases. We analyze collected data by using fixed effects regression methods and Wald tests for sensitivity check. We use fixed effects regression models for firms for determination of the connections between technology use in business and audit fees. We control for firm size, complexity, inherent risk, profitability and auditor quality. We chose fixed effects model as it makes possible to control for variables that have not or cannot be measured.

Keywords: audit fees, auditor quality, digitalization, Big4

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Authors: Emmanuel Benjamin, Matthias Blum

Abstract:

Purpose – We address smallholder farmers’ restricted possibilities to adopt sustainable technologies which have direct and indirect benefits. Smallholders often face little asset endowment due to small farm size und insecure property rights, therefore experiencing constraints in adopting agricultural innovation. A program involving payments for ecosystem services (PES) benefits poor smallholder farmers in developing countries in many ways and has been suggested as a means of easing smallholder farmers’ financial constraints. PES may also provide additional mainstay which can eventually result in more favorable credit contract terms due to the availability of collateral substitute. Results of this study may help to understand the barriers, motives and incentives for smallholders’ participation in PES and help in designing a strategy to foster participation in beneficial programs. Design/methodology/approach – This paper uses a random utility model and a logistic regression approach to investigate factors that influence agroforestry adoption. We investigate non-monetary factors, such as information spillover, that influence the decision to adopt such conservation strategies. We collected original data from non-government-run agroforestry mitigation programs with PES that have been implemented in the Mount Kenya region. Preliminary Findings – We find that spread of information, existing networks and peer involvement in such programs drive participation. Conversely, participation by smallholders does not seem to be influenced by education, land or asset endowment. Contrary to some existing literature, we found weak evidence for a positive correlation between the adoption of agroforestry with PES and age of smallholder, e.g., one increases with the other, in the Mount Kenyan region. Research implications – Poverty alleviation policies for developing countries should target social capital to increase the adoption rate of modern technologies amongst smallholders.

Keywords: agriculture innovation, agroforestry adoption, smallholders, payment for ecosystem services, Sub-Saharan Africa

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