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Comparison of Survival Analysis Approaches to Modelling Credit Risks
Sammy Mungasi,
Collins Odhiambo
Issue:
Volume 8, Issue 2, March 2019
Pages:
39-46
Received:
1 April 2019
Accepted:
15 May 2019
Published:
5 June 2019
Abstract: Credit risk is a critical area in finance and has drawn considerable research attention. As such, survival analysis has widely been used in credit risk, in particular to model debt’s time to default mechanisms. In this study, we revisit different survival analysis approaches as applied in credit risk defaulters’ data and assess their performance in light of the Kenyan context. In practice, inconsistency in validity of credit risk models used by many company when predicting and analysis of loan default is a common phenomenon that occurs unexpectedly. Loan defaults, often causes major loses to creditors’ and can be of great benefit if quantified correctly in advance by using correct models. Here, we address the unbiasedness, analysis and comparison of survival analysis approaches, particularly, the models of credit risk. We carry out data analysis using Cox proportional hazard model and it’s extensions as well as the mixture cure and non-cure model. We then compare the results systematically by investigating the most efficient and preferable model that produces best estimates in Kenyan real data setting. Results show, the Cox Proportional Hazard (CPH) model is more efficient in the analysis of Kenyan real data set compared to the frailty, the mixture cure and non-cure model.
Abstract: Credit risk is a critical area in finance and has drawn considerable research attention. As such, survival analysis has widely been used in credit risk, in particular to model debt’s time to default mechanisms. In this study, we revisit different survival analysis approaches as applied in credit risk defaulters’ data and assess their performance in...
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Bayesian Analysis of Retention and Graduation of Female Students of Higher Education Institution: The Case of Hawassa University (HU), Ethiopia
Tsega Kahsay Gebretekle,
Ayele Taye Goshu
Issue:
Volume 8, Issue 2, March 2019
Pages:
47-66
Received:
1 April 2019
Accepted:
23 May 2019
Published:
10 June 2019
Abstract: The study was conducted on female students who were 2005, 2006, 2007, and 2008 entries in the fields of Natural Science, Agriculture, and Social Science. From 1931 female students a sample of 605 was taken using stratified random sampling, Primary and secondary data were collected using questionnaire and analyzed using the Bayesian logistic regression analysis. The results showed that the percentage of graduation among 362 females who were enrolled in 2005, 2006, and 2007 was 72.1%. Similarly the retention rate among 243 females of 2008 entry was 75.7%. From the Bayesian logistic regression analyses, significant predicators of both graduation and retention were choice of field, preparatory average result, entrance exam score and first year cumulative GPA. Moreover pregnancy, organizing studying and leisure time, habit of chewing Khat, satisfaction with instructors, parent income, habit of smoking cigarette and using drugs, and feel safe to study at night in classrooms appeared as significant predictors of retention. The graduation rate and retention rate for the students who assigned to the field they did not choose were lower than that for those assigned to the field they chose. Those with first year CGPA less than 2.0 were having lower rates of graduation and retention than those having greater than 2.0. The graduation and retention rates for the students having higher preparatory average result and higher entrance exam score were higher than that for those having lower. The students having parents’ income less than 500 were less likely to retain than those having parents’ income greater than 1500. The retention rate for the students who were not satisfied with their instructors was lower than those were satisfied. The students who cannot organize their study and leisure time easily were less likely to retain than those can organize. In conclusion, the factors those mainly affect female students’ graduation and retention were more of academic variables; hence we recommend that assigning to the field they choose by their interest may help female students’ graduation and retention. The teaching method at secondary and preparatory schools should be designed to challenge and motivate them to adequately prepare them for Higher Education Institutions. Moreover, campus and Department administrators in collaboration with the students themselves and academic staff need to work hard to bring change in behavior, academics, and social aspects of female students at the University.
Abstract: The study was conducted on female students who were 2005, 2006, 2007, and 2008 entries in the fields of Natural Science, Agriculture, and Social Science. From 1931 female students a sample of 605 was taken using stratified random sampling, Primary and secondary data were collected using questionnaire and analyzed using the Bayesian logistic regress...
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Determinants of Food Security status in Rural Households in Mojaena Wodera Woreda, Ethiopia
Issue:
Volume 8, Issue 2, March 2019
Pages:
67-76
Received:
27 April 2019
Accepted:
2 June 2019
Published:
13 June 2019
Abstract: The main objective of this study was to assess household food security status and its major determinants in the rural households of Mojaena Wodera Woreda, Ethiopia. A systematic random sampling method was employed to select the sample from study area. The study period was from September 2017 to September 2018. The recommended daily calorie requirement was used to determine the household food security status. To analyze the data descriptive statistics, bivariate analysis and both Classical logistic regression and Bayesian logistic regression analyses were used. The descriptive analysis of the study revealed that only 37.6% of the sample households were food secured and 62.4% of households were food insecured which was felt short of the 2100kcal per day per person that was national recommended calorie requirements. Based on Hosmer and Lemeshow test the chi-square value and significance value shows that Classical logistic model is quite a good fit. In addition to this, the classification results revealed that 81.3% of the households were correctly predicted Using both Classical and Bayesian logistic regression analysis, eight out of twenty-one predictor variables were selected as major determinants of household food security status. These significant variables were age, marital status, farm land size, land fertility, annual yield, improved seed use, having oxen and family size of household head. Government and the woreda agricultural office should provide cultivable and more fertile farm land, improved seed and support oxen to the farming households at affordable prices to be able to increase farmland size and total annual yield or food production.
Abstract: The main objective of this study was to assess household food security status and its major determinants in the rural households of Mojaena Wodera Woreda, Ethiopia. A systematic random sampling method was employed to select the sample from study area. The study period was from September 2017 to September 2018. The recommended daily calorie requirem...
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Consistency Inference Property of QIC in Selecting the True Working Correlation Structure for Generalized Estimating Equations
Robert Nyamao Nyabwanga,
Fredrick Onyango,
Edgar Ouko Otumba
Issue:
Volume 8, Issue 2, March 2019
Pages:
77-84
Received:
28 March 2019
Accepted:
29 May 2019
Published:
29 June 2019
Abstract: The generalized estimating equations (GEE) is one of the statistical approaches for the analysis of longitudinal data with correlated response. A working correlation structure for the repeated measures of the outcome variable of a subject needs to be specified by this method and the GEE estimator for the regression parameter will be the most efficient if the working correlation matrix is correctly specified. Hence it is desirable to choose a working correlation matrix that is the closest to the underlying structure among a set of working correlation structures. The quasi-likelihood Information criteria (QIC) was proposed for the selection of the working correlation structure and the best subset of explanatory variables in GEE. However, its success rate in selecting the true correlation structure has been established to be about 29.4%. Likewise, past studies have shown that its bias increases with the number of parameters. By considering longitudinal data with binary response, we establish numerically through simulations the consistency property of QIC in selecting the true working correlation structure and the conditions for its consistency. Further, we propose a modified QIC that penalizes for the number of parameter estimates in the original QIC and numerically establish that the penalization enhances the consistency of QIC in selecting the true working correlation structure. The results indicate that QIC selects the true correlation structure with probability approaching one if only parsimonious structures are considered otherwise the selection rates are less than 50% regardless of the increase in the sample size, measurements per subject and level of correlation. Further, we established that the probability of selecting the true correlation structure R0 almost surely converges to one when we penalize for the number of correlation parameters estimated.
Abstract: The generalized estimating equations (GEE) is one of the statistical approaches for the analysis of longitudinal data with correlated response. A working correlation structure for the repeated measures of the outcome variable of a subject needs to be specified by this method and the GEE estimator for the regression parameter will be the most effici...
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