Search results for: poisson regression model

Authors: Nattabhorn Leamcharaskul

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This study aims to examine determining factors of the dynamics of poverty in Thailand by using panel data of 3,567 households in 2007-2017. Four techniques of estimation are employed to analyze the situation of poverty across households and time periods: the multinomial logit model, the sequential logit model, the quantile regression model, and the difference in difference model. Households are categorized based on their experiences into 5 groups, namely chronically poor, falling into poverty, re-entering into poverty, exiting from poverty and never poor households. Estimation results emphasize the effects of demographic and socioeconomic factors as well as unexpected events on the economic status of a household. It is found that remittances have positive impact on household’s economic status in that they are likely to lower the probability of falling into poverty or trapping in poverty while they tend to increase the probability of exiting from poverty. In addition, not only receiving a secondary source of household income can raise the probability of being a never poor household, but it also significantly increases household income per capita of the chronically poor and falling into poverty households. Public work programs are recommended as an important tool to relieve household financial burden and uncertainty and thus consequently increase a chance for households to escape from poverty.

Keywords: difference in difference, dynamic, multinomial logit model, panel data, poverty, quantile regression, remittance, sequential logit model, Thailand, transfer

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Authors: Alan Wan

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In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets.

Keywords: heteroscedasticity-robust, model averaging, multiplicative heteroscedasticity, plug-in, squared prediction risk

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Authors: Gianna Zou

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Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

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Authors: Fatima Zehra Allahverdi, Nukhet Bayer

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Objective: Three questions were formulated to examine stressful working units (intensive care units, emergency unit nurses) utilizing the self-perception theory and social support theory. This study provides a distinctive input by inspecting the combination of variables regarding stressful working environments. Method: The descriptive research was conducted with the participation of 400 nurses working at Ankara City Hospital. The study used Multivariate Analysis of Variance (MANOVA), regression analysis, and a mediation model. Hypothesis one used MANOVA followed by a Scheffe post hoc test. Hypothesis two utilized regression analysis using a hierarchical linear regression model. Hypothesis three used a mediation model. Result: The study utilized mediation analyses. Findings supported the hypotheses that intensive care units have significantly high scores in virtual communication and virtual tolerance. The number of years on the job, virtual communication, virtual tolerance, and phubbing significantly predicted 51% of the variance of perception of addiction. Interestingly, the number of years on the job, while significant, was negatively related to perception of addiction. Conclusion: The reasoning behind these findings and the lack of significance in the emergency unit is discussed. Around 7% of the variance of phubbing was accounted for through working in intensive care units. The model accounted for 26.80 % of the differences in the perception of addiction.

Keywords: phubbing, social media, working units, years on the job, stress

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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Authors: Kehinde Peter Oyeduntan, Kayode Oshinubi

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The west African ports have been experiencing large inflow and outflow of containerized cargo in the last decades, and this has created a quest amongst the countries to attain the status of hub port for the sub-region. This study analyzed the relationship between the container throughput and Gross Domestic Products (GDP) of nine west African countries, using Simple Linear Regression (SLR), Polynomial Regression Model (PRM) and Support Vector Machines (SVM) with a time series of 20 years. The results showed that there exists a high correlation between the GDP and container throughput. The model also predicted the container throughput in west Africa for the next 20 years. The findings and recommendations presented in this research will guide policy makers and help improve the management of container ports and terminals in west Africa, thereby boosting the economy.

Keywords: container, ports, terminals, throughput

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Authors: Jerzy Smardzewski

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Structures with negative Poisson's ratios are called auxetic. They are characterized by better mechanical properties than conventional structures, especially shear strength, the ability to better absorb energy and increase strength during bending, especially in sandwich panels. Commonly used paper cores of cellular boards are made of hexagonal cells. With isotropic facings, these cells provide isotropic properties of the entire furniture board. Shelves made of such panels with a thickness similar to standard chipboards do not provide adequate stiffness and strength of the furniture. However, it is possible to transform the shape of hexagonal cells into polyhedral auxetic cells that improve the mechanical properties of the core. The work aimed to transform the hexagonal cells of the paper core into auxetic cells and determine their basic mechanical properties. Using numerical methods, it was decided to design the most favorable proportions of cells distinguished by the lowest Poisson's ratio and the highest modulus of linear elasticity. Standard cores for cellular boards commonly used to produce 34 mm thick furniture boards were used for the tests. Poisson's ratios, bending strength, and linear elasticity moduli were determined for such cores and boards. Then, the cells were transformed into auxetic structures, and analogous cellular boards were made for which mechanical properties were determined. The results of numerical simulations for which the variable parameters were the dimensions of the cell walls, wall inclination angles, and relative cell density were presented in the further part of the paper. Experimental tests and numerical simulations showed the beneficial effect of auxeticization on the mechanical quality of furniture panels. They allowed for the selection of the optimal shape of auxetic core cells.

Keywords: auxetics, honeycomb, panels, simulation, experiment

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Authors: Adil Kaval, Digdem Muge Siyez

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With the negative effects of internet use in a person's life, the use of the Internet has become an issue. This subject was mostly considered as internet addiction, and it was investigated. In literature, it is noteworthy that some theoretical models have been proposed to explain the reasons for internet addiction. In addition to these theoretical models, it may be thought that the coping style for stressing events can be a predictor of internet addiction. It was aimed to test with logistic regression the effect of high school students' coping styles on internet addiction levels. Sample of the study consisted of 770 Turkish adolescents (471 girls, 299 boys) selected from high schools in the 2017-2018 academic year in İzmir province. Internet Addiction Test, Coping Scale for Child and Adolescents and a demographic information form were used in this study. The results of the logistic regression analysis indicated that the model of coping styles predicted internet addiction provides a statistically significant prediction of internet addiction. Gender does not predict whether or not to be addicted to the internet. The active coping style is not effective on internet addiction levels, while the avoiding and negative coping style are effective on internet addiction levels. With this model, % 79.1 of internet addiction in high school is estimated. The Negelkerke pseudo R2 indicated that the model accounted for %35 of the total variance. The results of this study on Turkish adolescents are similar to the results of other studies in the literature. It can be argued that avoiding and negative coping styles are important risk factors in the development of internet addiction.

Keywords: adolescents, coping, internet addiction, regression analysis

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Authors: Ana Clara Santos, Maria Manuela Portela, Bettina Schaefli

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This work assesses the performance of an analytical model framework to generate daily flow duration curves, FDCs, based on climatic characteristics of the catchments and on their streamflow recession coefficients. According to the analytical model framework, precipitation is considered to be a stochastic process, modeled as a marked Poisson process, and recession is considered to be deterministic, with parameters that can be computed based on different models. The analytical model framework was tested for three case studies with different hydrological regimes located in Switzerland: pluvial, snow-dominated and glacier. For that purpose, five time intervals were analyzed (the four meteorological seasons and the civil year) and two developments of the model were tested: one considering a linear recession model and the other adopting a nonlinear recession model. Those developments were combined with recession coefficients obtained from two different approaches: forward and inverse estimation. The performance of the analytical framework when considering forward parameter estimation is poor in comparison with the inverse estimation for both, linear and nonlinear models. For the pluvial catchment, the inverse estimation shows exceptional good results, especially for the nonlinear model, clearing suggesting that the model has the ability to describe FDCs. For the snow-dominated and glacier catchments the seasonal results are better than the annual ones suggesting that the model can describe streamflows in those conditions and that future efforts should focus on improving and combining seasonal curves instead of considering single annual ones.

Keywords: analytical streamflow distribution, stochastic process, linear and non-linear recession, hydrological modelling, daily discharges

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

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Authors: Petr Gurný

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One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

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Authors: Arun Goel

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The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: air entrainment rate, dissolved oxygen, weir, SVM, regression

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Authors: Michael Tilahun Mulugeta

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Pedestrians are the most vulnerable road users as they are more exposed to the risk of collusion. Pedestrian safety at road intersections still remains the most vital and yet unsolved issue in Addis Ababa, Ethiopia. One of the critical points in pedestrian safety is the occurrence of conflict between turning vehicle and pedestrians at un-signalized intersection. However, a better understanding of the factors that affect the likelihood of the conflicts would help provide direction for countermeasures aimed at reducing the number of crashes. This paper has sorted to explore a model to describe the relation between traffic conflicts and influencing factors using Multiple Linear regression methodology. In this research the main focus is to study the interaction of turning (left & right) vehicle with pedestrian at unsignalized intersections. The specific objectives also to determine factors that affect the number of potential conflicts and develop a model of potential conflict.

Keywords: potential, regression analysis, pedestrian, conflicts

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Authors: Tauha Hussain Ali, Shabir Hussain Khahro, Nafees Ahmed Memon

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Accurate prediction of project time for planning and bid preparation stage should contain realistic dates. Constructors use their experience to estimate the project duration for the new projects, which is based on intuitions. It has been a constant concern to both researchers and constructors to analyze the accurate prediction of project duration for bid preparation stage. In Pakistan, such study for time cost relationship has been lacked to predict duration performance for the construction projects. This study is an attempt to explore the time cost relationship that would conclude with a mathematical model to predict the time for the drainage rehabilitation projects in the province of Sindh, Pakistan. The data has been collected from National Engineering Services (NESPAK), Pakistan and regression analysis has been carried out for the analysis of results. Significant relationship has been found between time and cost of the construction projects in Sindh and the generated mathematical model can be used by the constructors to predict the project duration for the upcoming projects of same nature. This study also provides the professionals with a requisite knowledge to make decisions regarding project duration, which is significantly important to win the projects at the bid stage.

Keywords: BTC Model, project time, relationship of time cost, regression

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Authors: Lichung Jen, Yi Chun Liu, Kuan-Wei Lee

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Fruitful data has been accumulated in database nowadays and is commonly used as support for decision-making. In the healthcare industry, hospital, for instance, ordering pharmacy inventory is one of the key decision. With large drug inventory, the current cost increases and its expiration dates might lead to future issue, such as drug disposal and recycle. In contrast, underestimating demand of the pharmacy inventory, particularly standing drugs, affects the medical treatment and possibly hospital reputation. Prescription behaviour of hospital physicians is one of the critical factor influencing this decision, particularly irregular prescription behaviour. If a drug’s usage amount in the month is irregular and less than the regular usage, it may cause the trend of subsequent stockpiling. On the contrary, if a drug has been prescribed often than expected, it may result in insufficient inventory. We proposed a hierarchical Bayesian mixture model with two components to identify physicians’ regular/irregular prescription patterns with probabilities. Heterogeneity of hospital is considered in our proposed hierarchical Bayes model. The result suggested that modeling the prescription patterns of physician is beneficial for estimating the order quantity of medication and pharmacy inventory management of the hospital. Managerial implication and future research are discussed.

Keywords: hierarchical Bayesian model, poission mixture model, medicines prescription behavior, irregular behavior

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Md. Rashed Aalm, Md. Nuruzzaman Khan, Yothin Sawangdee

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Background: Around 47% of the total pregnancies are unintended in Bangladesh, which lead to several adverse consequences, including maternal and child mortality. Use of menstrual regulation (MR) can help women to reduce unintended pregnancy related adverse consequences. We explored the prevalence and determinants of MR services among ever-married women in Bangladesh. Methods: Total of 14,346 ever-married women data were analysed from the 2017 Bangladesh Demographic and Health Survey. Our study variable was use or non-use of MR services. Individual, household, and community level factors were the explanatory factors. Multilevel mixed-effect Poisson regression model was used to determine the factors associated with MR services in Bangladesh. Results: Nearly 7% of the total women in Bangladesh use MR services. Use of MR services was found higher among women who were aged 20-30 ages (IRR 1.60, 95% CI: 1.17–2.17), who were overweight (IRR 1.43, 95% CI: 1.13–1.81), had at least 1 child (IRR 2.97, 95% CI: 2.34– 3.77) or > 2 children (IRR 3.22, 95% CI: 2.45–4.20), and the birth preceding birth interval was(2 – 4) years (IRR 1.56, 95% CI: 1.13–2.15). Around 1.39 times (95% CI: 1.11–1.73) higher likelihood of MR was found among women whose husbands were engage with business. At the community level, MR service was found lower among the women who resided in the community with higherilliteracy (IRR 0.67, 95% CI: 0.42–0.96) and the Mymensingh division (IRR 0.39, 95% CI: 0.31–0.91). Conclusion: Use of MR service is comparatively low, which indicate a significant proportion of unintended pregnancy continued toward life-birth. This could be responsible for higher adverse maternal and child health outcomes in Bangladesh. Initiatives should be taken to ensure MR services is available when women need this service.

Keywords: menstrual regulation, pattern, risk, maternal health, Bangladesh

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Authors: A. Porshnev, A. Zaporozhtchuk

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Motivation regarded as a key factor of labor turnover, is especially important for volunteers working on an altruistic basis in NGO. Despite the motivational letter, candidate selection depends on the impression of the selection committee, which can be subject to human bias. We expect that structured and unstructured information provided in motivation letters could be used to improve candidate selection procedures. In our paper, we perform qualitative and quantitative analysis of 2280 motivation letters, create logistic regression, and build a decision tree to improve selection procedures. Our analysis showed that motivation factors are significant and enable human resources department to forecast labor turnover and provide extra information to demographic, professional and timing questions. In spite of the average level of accuracy the model demonstrates the selection procedures of company of under consideration can be improved. We also discuss interrelation between answers to open and closed motivation questions, recommend changes in motivational letter templates to ensure more relevant information about applicants and further steps to create more accurate model.

Keywords: decision trees, logistic regression, model, motivational letter, non-governmental organization, retention, turnover

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Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

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Authors: Danielle M. Barna, Kolbjørn Engeland, Thordis Thorarinsdottir, Chong-Yu Xu

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Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Often design flood values are needed at locations with insufficient data. Additionally, in hydrologic applications where flood retention is important (e.g., floodplain management and reservoir design), design flood values are required at different flood durations. A statistical approach to this problem is a development of a regression model for extremes where some of the parameters are dependent on flood duration in addition to being covariate-dependent. In hydrology, this is called a regional flood-duration-frequency (regional-QDF) model. Typically, the underlying statistical distribution is chosen to be the Generalized Extreme Value (GEV) distribution. However, as the support of the GEV distribution depends on both its parameters and the range of the data, special care must be taken with the development of the regional model. In particular, we find that the GEV is problematic when developing a GAMLSS-type analysis due to the difficulty of proposing a link function that is independent of the unknown parameters and the observed data. We discuss these challenges in the context of developing a regional QDF model for Norway.

Keywords: design flood values, bayesian statistics, regression modeling of extremes, extreme value analysis, GEV

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Authors: Sachin Jain, Daniel Perez-Astudillo, Dunia A. Bachour, Antonio P. Sanfilippo

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The future of solar energy in Qatar is evolving steadily. Hence, high-quality spatial solar radiation data is of the uttermost requirement for any planning and commissioning of solar technology. Generally, two types of solar radiation data are available: satellite data and ground observations. Satellite solar radiation data is developed by the physical and statistical model. Ground data is collected by solar radiation measurement stations. The ground data is of high quality. However, they are limited to distributed point locations with the high cost of installation and maintenance for the ground stations. On the other hand, satellite solar radiation data is continuous and available throughout geographical locations, but they are relatively less accurate than ground data. To utilize the advantage of both data, a product has been developed here which provides spatial continuity and higher accuracy than any of the data alone. The popular satellite databases: National Solar radiation Data Base, NSRDB (PSM V3 model, spatial resolution: 4 km) is chosen here for merging with ground-measured solar radiation measurement in Qatar. The spatial distribution of ground solar radiation measurement stations is comprehensive in Qatar, with a network of 13 ground stations. The monthly average of the daily total Global Horizontal Irradiation (GHI) component from ground and satellite data is used for error analysis. The normalized root means square error (NRMSE) values of 3.31%, 6.53%, and 6.63% for October, November, and December 2019 were observed respectively when comparing in-situ and NSRDB data. The method is based on the Empirical Bayesian Kriging Regression Prediction model available in ArcGIS, ESRI. The workflow of the algorithm is based on the combination of regression and kriging methods. A regression model (OLS, ordinary least square) is fitted between the ground and NSBRD data points. A semi-variogram is fitted into the experimental semi-variogram obtained from the residuals. The kriging residuals obtained after fitting the semi-variogram model were added to NSRBD data predicted values obtained from the regression model to obtain the final predicted values. The NRMSE values obtained after merging are respectively 1.84%, 1.28%, and 1.81% for October, November, and December 2019. One more explanatory variable, that is the ground elevation, has been incorporated in the regression and kriging methods to reduce the error and to provide higher spatial resolution (30 m). The final GHI maps have been created after merging, and NRMSE values of 1.24%, 1.28%, and 1.28% have been observed for October, November, and December 2019, respectively. The proposed merging method has proven as a highly accurate method. An additional method is also proposed here to generate calibrated maps by using regression and kriging model and further to use the calibrated model to generate solar radiation maps from the explanatory variable only when not enough historical ground data is available for long-term analysis. The NRMSE values obtained after the comparison of the calibrated maps with ground data are 5.60% and 5.31% for November and December 2019 month respectively.

Keywords: global horizontal irradiation, GIS, empirical bayesian kriging regression prediction, NSRDB

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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Traditional execution process follows the path of execution drawn by the process analyst without observing the behaviour of resource and other real-time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: αyM iner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending the probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique to identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour αyM iner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimised by 59% by decreasing their load.

Keywords: cross-organization process mining, process behaviour, path of execution, polynomial regression model

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Authors: Abdelmadjid Mammeri, Fatima Zohra Mahi, Luca Varani, H. Marinchoi

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We propose an analytical model for the admittance and the noise calculations of the InGaAs transistor and diode. The development of the small-signal admittance takes into account the longitudinal and transverse electric fields through a pseudo two-dimensional approximation of the Poisson equation. The frequency-dependent of the small-signal admittance response is determined by the total currents and the potentials matrix relation between the gate and the drain terminals. The noise is evaluated by using the real part of the transistor/diode admittance under a small-signal perturbation. The analytical results show that the admittance spectrum exhibits a series of resonant peaks corresponding to the excitation of plasma waves. The appearance of the resonance is discussed and analyzed as functions of the channel length and the temperature. The model can be used, on one hand; to control the appearance of the plasma resonances, and on other hand; can give significant information about the noise frequency dependence in the InGaAs transistor and diode.

Keywords: InGaAs transistors, InGaAs diode, admittance, resonant peaks, plasma waves, analytical model

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Authors: Ishapathik Das

Abstract:

The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: A. Ishag Mohamed, A. A. Rabah

Abstract:

The objective of this research is to develop a generalized correlation for the prediction of thermal conductivity of n-Alkanes and Alkenes. There is a minority of research and lack of correlation for thermal conductivity of liquids in the open literature. The available experimental data are collected covering the groups of n-Alkanes and Alkenes.The data were assumed to correlate to temperature using Filippov correlation. Nonparametric regression of Grace Algorithm was used to develop the generalized correlation model. A spread sheet program based on Microsoft Excel was used to plot and calculate the value of the coefficients. The results obtained were compared with the data that found in Perry's Chemical Engineering Hand Book. The experimental data correlated to the temperature ranged "between" 273.15 to 673.15 K, with R2 = 0.99.The developed correlation reproduced experimental data that which were not included in regression with absolute average percent deviation (AAPD) of less than 7 %. Thus the spread sheet was quite accurate which produces reliable data.

Keywords: N-Alkanes, N-Alkenes, nonparametric, regression

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Authors: Yehya Rasool, Mohit Agrawal

Abstract:

The site characterization and site response analysis are the crucial steps for reliable seismic microzonation of an area. So, the basic parameters involved in these fundamental steps are required to be chosen properly in order to efficiently characterize the vulnerable sites of the study region. In this study, efforts are made to delineate the variations in the physical parameter of the soil for the summer and monsoon seasons of the year (2021) by using Horizontal-to-Vertical Spectral Ratios (HVSRs) recorded at five sites of the Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India. The data recording at each site was done in such a way that less amount of anthropogenic noise was recorded at each site. The analysis has been done for five seismic parameters like predominant frequency, H/V ratio, the phase velocity of Rayleigh waves, shear wave velocity (Vs), compressional wave velocity (Vp), and Poisson’s ratio for both the seasons of the year. From the results, it is observed that these parameters majorly vary drastically for the upper layers of soil, which in turn may affect the amplification ratios and probability of exceedance obtained from seismic hazard studies. The HVSR peak comes out to be higher in monsoon, with a shift in predominant frequency as compared to the summer season of the year 2021. Also, the drastic reduction in shear wave velocity (up to ~10 m) of approximately 7%-15% is also perceived during the monsoon period with a slight decrease in compressional wave velocity. Generally, the increase in the Poisson ratios is found to have higher values during monsoon in comparison to the summer period. Our study may be very beneficial to various agricultural and geotechnical engineering projects.

Keywords: HVSR, shear wave velocity profile, Poisson ratio, microtremor data

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Authors: Mohammad Abu-Shaira, Weishi Shi

Abstract:

Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift.

Keywords: automated drift detection and adaptation, concept drift, hyperparameters optimization, online and adaptive learning, regression

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Authors: Sergio Haram Sarmiento, Arcady Ponosov

Abstract:

Based on appropriate multivariate statistical methodology, we suggest a generic framework for efficient parameter estimation for ordinary differential equations and the corresponding nonlinear models. In this framework classical linear regression strategies is refined into a nonlinear regression by a locally linear modelling technique (known as metamodelling). The approach identifies those latent variables of the given model that accumulate most information about it among all approximations of the same dimension. The method is applied to several benchmark problems, in particular, to the so-called ”power-law systems”, being non-linear differential equations typically used in Biochemical System Theory.

Keywords: principal component analysis, generalized law of mass action, parameter estimation, metamodels

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Authors: Innocent Pangapanga, Eric Mungatana

Abstract:

Fall armyworm (FAW) (Spodoptera frugiperda), an invasive lepidopteran pest, has caused substantial yield loss since its first detection in September 2016, thereby threatening the farm productivity food security and poverty reduction initiatives in Malawi. Several stakeholders, including households, have adopted chemical pesticides to control FAW without accounting for its costs on welfare, health and the environment. Thus, this study has used panel data endogenous switching regression model to investigate the impact of FAW and the integrated pest management (IPM) –related practices on-farm productivity and food security. The study finds that FAW substantively reduces farm productivity by seven (7) percent and influences the adoption of IPM –related practices, namely, intercropping, mulching, and agroforestry, by 6 percent, ceteris paribus. Interestingly, multiple adoptions of the IPM -related practices noticeably increase farm productivity by 21 percent. After accounting for potential endogeneity through the endogenous switching regression model, the IPM practices further demonstrate tenfold more improvement on food security, implying the role of the IPM –related practices in containing the effect of FAW at the household level.

Keywords: hunger, invasive fall army worms, integrated pest management practices, farm productivity, endogenous switching regression

Procedia PDF Downloads 138