Search results for: conditional logistic regression

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: 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: Oscar E. Cariceo

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In Chile, there is a lack of evidence about the impact of polyvictimization on the emergence of suicidal thoughts among children and young people. Thus, this study aims to explore the association between the episodes of polyvictimization suffered by Chilean children and young people and the manifestation of signs related to suicidal tendencies. To achieve this purpose, secondary data from the First Polyvictimization Survey on Children and Adolescents of 2017 were analyzed, and a binomial logistic regression model was applied to establish the probability that young people are experiencing suicidal ideation episodes. The main findings show that women between the ages of 13 and 15 years, who are in seventh grade and second in subsidized schools, are more likely to express suicidal ideas, which increases if they have suffered different types of victimization, particularly physical violence, psychological aggression, and sexual abuse.

Keywords: Chile, polyvictimization, suicidal ideation, youth

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Authors: Hurgesa Hundera, Samuel Shibeshibikeko, Tarike Daba, Tesfaye Ganamo

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The study aimed at examining the ongoing adaptation strategies used by smallholder farmers in response to climate variability in Boset district. It also assessed the socioeconomic factors that influence the choice of adaptation strategies of smallholder farmers to climate variability risk. For attaining the objectives of the study, both primary and secondary sources of data were employed. The primary data were obtained through a household questionnaire, key informant interviews, focus group discussions, and observations, while secondary data were acquired through desk review. Questionnaires were distributed and filled by 328 respondents, and they were identified through systematic random sampling technique. Descriptive statistics and binary logistic regression model were applied in this study as the main analytical methods. The findings of the study reveal that the sample households have utilized multiple adaptation strategies in response to climate variability, such as cropping early mature crops, planting drought resistant crops, growing mixed crops on the same farm lands, and others. The results of the binary logistic model revealed that education, sex, age, family size, off farm income, farm experience, access to climate information, access to farm input, and farm size were significant and key factors determining farmers’ choice of adaptation strategies to climate variability in the study area. To enable effective adaptation measures, Ministry of Agriculture and Natural Resource, with its regional bureaus and offices and concerned non–governmental organizations, should consider climate variability in their planning and budgeting in all levels of decision making.

Keywords: adaptation strategies, boset district, climate variability, smallholder farmers

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Authors: Sarkar Humayun Kabir

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This paper aims to investigate whether ethical investment instruments could contribute to stability in financial markets. In order to address the main issue, the study investigates the stability of return in seven conventional and Islamic equity markets of Asia, Europe and North America and in five major commodity markets starting from 1996 to June 2012. In addition, the study examines the unconditional correlation between returns of the assets under review to investigate portfolio diversification benefits of investors. Applying relevant methods, the study finds that investors may enjoy sustainable returns from their portfolios by investing in ethical financial instruments such as Islamic equities. In addition, it should be noted that most of the commodities, gold in particular, are either low or negatively correlated with equity returns. These results suggest that investors would be better off by investing in portfolios combining Islamic equities and commodities in general. The sustainable returns of ethical investments has important implications for the investors and markets since these investments can provide stable returns while the investors can avoid production of goods and services which believes to be harmful for human and the society as a whole.

Keywords: financial sustainability, ethical investment instruments, islamic equity, dynamic conditional correlation, conditional volatility

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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: Rancy Chepchirchir Kosgey, John Olukuru

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This study examines the impact of introduction of futures trading on the spot price volatility in the commodity market. The paper considers the United States of America, South Africa and Ethiopian economies. Three commodities i.e. coffee, maize and wheat from New York Merchantile Exchange, South African Futures Exchange and Ethiopian Commodity Exchange are analyzed. ARCH LM test is used to check for heteroskedasticity and GARCH and EGARCH are used to check for the behavior of volatility between the pre- and post-futures periods. For all the three economies, the results indicate presence of the ARCH effect in the log returns. For conditional and unconditional variances; spot price volatility for coffee has decreased after futures trading in all the economies and the EGARCH has also shown reduction in persistence of volatility in the post-futures period in the three economies; while that of maize has reduced for the Ethiopian economy while there has been an increase in both the US and South African economies. For wheat, the conditional variance has been found to rise in the post-futures period in all the three economies.

Keywords: derivatives, futures exchange, agricultural commodities, spot price volatility

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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: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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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: 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: Kanchan Negi

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Background: Modern and urban occupational culture have driven demands for people to work long hours and weekends and take work to home at times. Research on the health effects of these exhaustive temporal work patterns is scant or contradictory. This study examines the relationship between work patterns and wellbeing in a sample of women living in the metropolitan hub of Delhi. Method: This study is based on the data collected from 360 currently married women between age 29 and 49 years, working in the urban capital hub of India, i.e., Delhi. The women interviewed were professionals from the education, health, banking and information and technology (IT) sector. Bivariate analysis was done to study the characteristics of the sample. Logistic regression analysis was used to estimate the physical and psychological wellbeing across occupational characteristics. Results: Most of the working women were below age 35 years; around 30% of women worked in the education sector, 23% in health, 21% in banking and 26% in the IT sector. Over 55% of women were employed in the private sector and only 36% were permanent employees. Nearly 30% of women worked for more than the standard 8 hours a day. The findings from logistic regression showed that compared to women working in the education sector, those who worked in the banking and IT sector more likely to have physical and psychological health issues (OR 2.07-4.37, CI 1.17-4.37); women who bear dual burden of responsibilities had higher odds of physical and psychological health issues than women who did not (OR 1.19-1.85 CI 0.96-2.92). Women who worked for more than 8 hours a day (OR 1.15, CI 1.01-1.30) and those who worked for more than five days a week (OR 1.25, CI 1.05-1.35) were more likely to have physical health issues than women who worked for 6-8 hours a day and five days e week, respectively. Also, not having flexible work timings and compensatory holidays increased the odds of having physical and psychological health issues among working women (OR 1.17-1.29, CI 1.01-1.47). Women who worked in the private sector, those employed temporarily and who worked in the non-conducive environments were more likely to have psychological health issues as compared to women in the public sector, permanent employees and those who worked in a conducive environment, respectively (OR 1.33-1.67, CI 1.09-2.91). Women who did not have poor work-life balance had reduced the odds of psychological health issues than women with poor work-life balance (OR 0.46, CI 0.25-0.84). Conclusion: Poor wellbeing significantly linked to strenuous and rigid work patterns, suggesting that modern and urban work culture may contribute to the poor wellbeing of working women. Noticing the recent decline in female workforce participation in Delhi, schemes like Flexi-timings, compensatory holidays, work-from-home and daycare facilities for young ones must be welcomed; these policies already exist in some private sector firms, and the public sectors companies should also adopt such changes to ease the dual burden as homemaker and career maker. This could encourage women in the urban areas to readily take up the jobs with less juggle to manage home and work.

Keywords: occupational characteristics, urban India, well-being, working women

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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: 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: Francisco Fernandez de Castro

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The Conditional Cash Transfer (CCT) model gives monetary transfers to beneficiary families on the condition that they take specific education and health actions. According to the economic rationale of CCTs the poor need incentives to invest in their human capital because they are trapped by a lack of information and misleading beliefs. If left to their own decision, the poor will not be able to choose what is in their best interests. The basic assumption of the CCT model is that the poor need incentives to take care of their own education and health-nutrition. Due to the incentives (income cash transfers and conditionalities), beneficiary families are supposed to attend doctor visits and health talks. Children would stay in the school. These incentivized behaviors would produce outcomes such as better health and higher level of education, which in turn will reduce poverty. Based on a grounded theory approach to conduct a two-year period of qualitative data collection in northern Mexico, this study shows that this explanation is incomplete. In addition to the information failure and inadequate beliefs, there are structural barriers in everyday life of households that make health-nutrition and education investments difficult. In-depth interviews and observation work showed that the program takes for granted local conditions in which beneficiary families should fulfill their co-responsibilities. Data challenged the program’s assumptions and unveiled local obstacles not contemplated in the program’s design. These findings have policy and research implications for the CCT agenda. They bring elements for late programming due to the gap between the CCT strategy as envisioned by policy designers, and the program that beneficiary families experience on the ground. As for research consequences, these findings suggest new avenues for scholarly work regarding the causal mechanisms and social processes explaining CCT outcomes.

Keywords: conditional cash transfers, incentives, poverty, structural barriers

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Authors: Fatemeh Valizadeh Gamchi, Edward L. Boone

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This research focuses on Lyme disease, a widespread infectious condition in the United States caused by the bacterium Borrelia burgdorferi sensu stricto. It is critical to identify environmental and economic elements that are contributing to the spread of the disease. This study examined data from Virginia to identify a subset of explanatory variables significant for Lyme disease case numbers. To identify relevant variables and avoid overfitting, linear poisson, and regularization regression methods such as a ridge, lasso, and elastic net penalty were employed. Cross-validation was performed to acquire tuning parameters. The methods proposed can automatically identify relevant disease count covariates. The efficacy of the techniques was assessed using four criteria on three simulated datasets. Finally, using the Virginia Department of Health’s Lyme disease data set, the study successfully identified key factors, and the results were consistent with previous studies.

Keywords: lyme disease, Poisson generalized linear model, ridge regression, lasso regression, elastic net regression

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Authors: Muhammad Bahrul Ilmi

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The purpose of this research is to analyze the influence of Islamic financing and work relation founding simultaneously and partially towards non-performing financing in Islamic banks. This research was regression quantitative field research, and had been done in Muammalat Indonesia Bank and Islamic Danamon Bank in 3 months. The populations of this research were 15 account officers of Muammalat Indonesia Bank and Islamic Danamon Bank in Surakarta, Indonesia. The techniques of collecting data used in this research were documentation, questionnaire, literary study and interview. Regression analysis result shows that Islamic financing and work relation founding simultaneously has positive and significant effect towards non performing financing of two Islamic Banks. It is obtained with probability value 0.003 which is less than 0.05 and F value 9.584. The analysis result of Islamic financing regression towards non performing financing shows the significant effect. It is supported by double linear regression analysis with probability value 0.001 which is less than 0.05. The regression analysis of work relation founding effect towards non-performing financing shows insignificant effect. This is shown in the double linear regression analysis with probability value 0.161 which is bigger than 0.05.

Keywords: Syariah financing, work relation founding, non-performing financing (NPF), Islamic Bank

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Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

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In urban area, several landmarks may affect housing price and rents, hedonic analysis should employ distance variables corresponding to each landmarks. Unfortunately, the effects of distances to landmarks on housing prices are generally not consistent with the true price. These distance variables may cause magnitude error in regression, pointing a problem of spatial multicollinearity. In this paper, we provided some approaches for getting the samples with less bias and method on locating the specific sampling area to avoid the multicollinerity problem in two specific landmarks case.

Keywords: landmarks, hedonic regression, distance variables, collinearity, multicollinerity

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

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Authors: Aishvarya Gupta, Keerthana Anand, Sasirekha Rengaraj, Latha Chathurvedula

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Introduction: Advances in assisted reproductive technology and increase in the proportion of infertile couples have both contributed to the steep increase in the incidence of twin pregnancies in past decades. Maternal and perinatal complications are higher in twins than in singleton pregnancies. Studies comparing the maternal and perinatal outcomes of ART twin pregnancies versus spontaneously conceived twin pregnancies report heterogeneous results making it unclear whether the complications are due to twin gestation per se or because of assisted reproductive techniques. The present study aims to compare both maternal and perinatal outcomes in twin pregnancies which are spontaneously conceived and after assisted conception methods, so that targeted steps can be undertaken in order to improve maternal and perinatal outcome of twins. Objectives: To study perinatal and maternal outcome in twin pregnancies conceived spontaneously as well as with assisted methods and compare the outcomes between the two groups. Setting: Women delivering at JIPMER (tertiary care institute), Pondicherry. Population: 380 women with twin pregnancies who delivered in JIPMER between June 2015 and March 2017 were included in the study. Methods: The study population was divided into two cohorts – one conceived by spontaneous conception and other by assisted reproductive methods. Association of various maternal and perinatal outcomes with the method of conception was assessed using chi square test or Student's t test as appropriate. Multiple logistic regression analysis was done to assess the independent association of assisted conception with maternal outcomes after adjusting for age, parity and BMI. Multiple logistic regression analysis was done to assess the independent association of assisted conception with perinatal outcomes after adjusting for age, parity, BMI, chorionicity, gestational age at delivery and presence of hypertension or gestational diabetes in the mother. A p value of < 0.05 was considered as significant. Result: There was increased proportion of women with GDM (21% v/s 4.29%) and premature rupture of membranes (35% v/s 22.85%) in the assisted conception group and more anemic women in the spontaneous group (71.27% v/s 55.1%). However assisted conception per se increased the incidence of GDM among twin gestations (OR 3.39, 95% CI 1.34 – 8.61) and did not influence any of the other maternal outcomes. Among the perinatal outcomes, assisted conception per se increased the risk of having very preterm (<32 weeks) neonates (OR 3.013, 95% CI 1.432 – 6.337). The mean birth weight did not significantly differ between the two groups (p = 0.429). Though there were higher proportion of babies admitted to NICU in the assisted conception group (48.48% v/s 36.43%), assisted conception per se did not increase the risk of admission to NICU (OR 1.23, 95% CI 0.76 – 1.98). There was no significant difference in perinatal mortality rates between the two groups (p = 0.829). Conclusion: Assisted conception per se increases the risk of developing GDM in women with twin gestation and increases the risk of delivering very preterm babies. Hence measures should be taken to ensure appropriate screening methods for GDM and suitable neonatal care in such pregnancies.

Keywords: assisted conception, maternal outcomes, perinatal outcomes, twin gestation

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Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

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Authors: Rajeshwar Singh

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The presence of multicollinearity is common in handling with several explanatory variables simultaneously due to exhibiting a linear relationship among them. A great problem arises in understanding the impact of explanatory variables on the dependent variable. Thus, the method of least squares estimation gives inexact estimates. In this case, it is advised to detect its presence first before proceeding further. Using the ridge regression degree of its occurrence is reduced but principal components regression gives good estimates in this situation. This paper discusses well-known techniques of the ridge and principal components regressions and applies to get the estimates of coefficients by both techniques. In addition to it, this paper also discusses the conflicting claim on the discovery of the method of ridge regression based on available documents.

Keywords: conflicting claim on credit of discovery of ridge regression, multicollinearity, principal components and ridge regressions, variance inflation factor

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Authors: Nabiha Bouafia, Asma Ben Cheikh, Asma Ammar, Olfa Ezzi, Mohamed Mahjoub, Khaoula Meddeb, Imed Chouchene, Hamadi Boussarsar, Mansour Njah

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Background: Hospital acquired infections (HAI) cause significant morbidity, mortality, length of stay and hospital costs, especially in the intensive care unit (ICU), because of the debilitated immune systems of their patients and exposure to invasive devices. The aims of this study were to determine the rate and the risk factors of HAI in an ICU of a university hospital in Tunisia. Materials/Methods: A prospective study was conducted in the 8-bed adult medical ICU of a University Hospital (Sousse Tunisia) during 14 months from September 15th, 2015 to November 15th, 2016. Patients admitted for more than 48h were included. Their surveillance was stopped after the discharge from ICU or death. HAIs were defined according to standard Centers for Disease Control and Prevention criteria. Risk factors were analyzed by conditional stepwise logistic regression. The p-value of < 0.05 was considered significant. Results: During the study, 192 patients had admitted for more than 48 hours. Their mean age was 59.3± 18.20 years and 57.1% were male. Acute respiratory failure was the main reason of admission (72%). The mean SAPS II score calculated at admission was 32.5 ± 14 (range: 6 - 78). The exposure to the mechanical ventilation (MV) and the central venous catheter were observed in 169 (88 %) and 144 (75 %) patients, respectively. Seventy-three patients (38.02%) developed 94 HAIs. The incidence density of HAIs was 41.53 per 1000 patient day. Mortality rate in patients with HAIs was 65.8 %( n= 48). Regarding the type of infection, Ventilator Associated Pneumoniae (VAP) and central venous catheter Associated Infections (CVC AI) were the most frequent with Incidence density: 14.88/1000 days of MV for VAP and 20.02/1000 CVC days for CVC AI. There were 5 Peripheral Venous Catheter Associated Infections, 2 urinary tract infections, and 21 other HAIs. Gram-negative bacteria were the most common germs identified in HAIs: Multidrug resistant Acinetobacter Baumanii (45%) and Klebsiella pneumoniae (10.96%) were the most frequently isolated. Univariate analysis showed that transfer from another hospital department (p= 0.001), intubation (p < 10-4), tracheostomy (p < 10-4), age (p=0.028), grade of acute respiratory failure (p=0.01), duration of sedation (p < 10-4), number of CVC (p < 10-4), length of mechanical ventilation (p < 10-4) and length of stay (p < 10-4), were associated to high risk of HAIS in ICU. Multivariate analysis reveals that independent risk factors for HAIs are: transfer from another hospital department: OR=13.44, IC 95% [3.9, 44.2], p < 10-4, duration of sedation: OR= 1.18, IC 95% [1.049, 1.325], p=0.006, high number of CVC: OR=2.78, IC 95% [1.73, 4.487], p < 10-4, and length of stay in ICU: OR= 1.14, IC 95% [1.066,1.22], p < 10-4. Conclusion: Prevention of nosocomial infections in ICUs is a priority of health care systems all around the world. Yet, their control requires an understanding of epidemiological data collected in these units.

Keywords: healthcare associated infections, incidence, intensive care unit, risk factors

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Authors: Nini Johana Marin Rodriguez

Abstract:

This paper applies DCC, EWMA and OGARCH models to compare the dynamic correlations between exchange rates, oil prices, exchange rates and stock markets to examine the time-varying conditional correlations to the daily oil prices and index returns in relation to the US dollar/local currency for developed (Canada and Mexico) and emerging Latin American markets (Brazil, Chile, Colombia and Peru). Changes in correlation interactions are indicative of structural changes in market linkages with implications to contagion and interdependence. For each pair of stock price-exchange rate and oil price-US dollar/local currency, empirical evidence confirms of a strengthening negative correlation in the last decade. Methodologies suggest only two events have significatively impact in the countries analyzed: global financial crisis and Europe crisis, both events are associated with shifts of correlations to stronger negative level for most of the pairs analyzed. While, the first event has a shifting effect on mainly emerging members, the latter affects developed members. The identification of these relationships provides benefits in risk diversification and inflation targeting.

Keywords: crude oil, dynamic conditional correlation, exchange rates, interdependence, stock prices

Procedia PDF Downloads 288

Authors: Qiang Xu, Xingxin Wu, Wenjie Guo, Xingqi Wang, Yang Sun, Renxiang Tan

Abstract:

The phosphatase SHP-2 is known to exert regulatory activities on cytokine receptor signaling and the dysregulation of SHP-2 has been implicated in the pathogenesis of a variety of diseases. Here we report several small molecule regulators of SHP-2 for the treatment of T cell-mediated diseases. The new cyclodepsipeptide trichomides A, isolated from the fermentation products of Trichothecium roseum, increased the phosphorylation of SHP-2 in activated T cells, and ameliorated contact dermatitis in mice. The trichomides A’s effects were significantly reversed by using the SHP-2-specific inhibitor PHPS1 or T cell-conditional SHP-2 knockout mice. Another compound is a cerebroside Fusaruside isolated from the endophytic fungus Fusarium sp. IFB-121. Fusaruside also triggered the tyrosine phosphorylation of SHP-2, which provided a possible mean of selectively targeting STAT1 for the treatment of Th1 cell-mediated inflammation and led to the discovery of the non-phosphatase-like function of SHP-2. Namely, the Fusaruside-activated pY-SHP-2 selectively sequestrated the cytosolic STAT1 to prevent its recruitment to IFN-R, which contributed to the improvement of experimental colitis in mice. Blocking the pY-SHP-2-STAT1 interaction, with SHP-2 inhibitor NSC-87877 or using T cells from conditional SHP-2 knockout mice, reversed the effects of fusaruside. Furthermore, the fusaruside’s effect is independent of the phosphatase activity of SHP-2, demonstrating a novel role for SHP-2 in regulating STAT1 signaling and Th1-type immune responses.

Keywords: SHP-2, small molecules, T cell, T cell-mediated diseases

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Authors: Waleed El-Said Abou El-Amaiem

Abstract:

Sole hemorrhages are considered as a main cause for sub clinical laminitis. In this study we aimed at discussing the most prominent risk factors associated with sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows. The final multivariate logistic regression model showed, a significant association between sub acute ruminal acidosis (P< 0.05), limb affected (P< 0.05) and weight (P< 0.05) and sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows. According to our knowledge, this is the first paper to discuss the risk factors associated with sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows.

Keywords: lameness, buffalo, sole hemorrhages, breed cows

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Authors: A. Bekbaev, M. Dzhamanbaev, R. Abitaeva, A. Karbozova, G. Nabyeva

Abstract:

In this paper, the regression dependence of dancing intensity from wind speed and length of span was established due to the statistic data obtained from multi-year observations on line wires dancing accumulated by power systems of Kazakhstan and the Russian Federation. The lower and upper limitations of the equations parameters were estimated, as well as the adequacy of the regression model. The constructed model will be used in research of dancing phenomena for the development of methods and means of protection against dancing and for zoning plan of the territories of line wire dancing.

Keywords: power lines, line wire dancing, dancing intensity, regression equation, dancing area intensity

Procedia PDF Downloads 293

Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

Abstract:

In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

Procedia PDF Downloads 99