Browsing by Author "Mbunzi, Stephen M."

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    Multinomial Logistic Modelling of Socio-Economic Factors Influencing Spending Behavior of University Students
    (2019-06) Akelo, Jacqueline Gogo; Mbunzi, Stephen M.; Ngari, Cyrus G.
    This study aims at determining the use of Multinomial Logistic Regression (MLR) model which is one of the important methods for categorical data analysis. This model particularly deals with one nominal or ordinal response variable that has more than two categories. Despite the fact that many researchers have applied this model in data analysis in many areas, for instance behavioral, social, health, and educational, a study on spending habits of University students have never been done. To identify the model by practical way, we conducted a survey research among students from University of Embu. Segment of the population of students in undergraduate level, a sample of 376 was selected. We employed the use stratified random sampling and simple random sampling without replacement in each stratum. The response variable consisted of five categories. Four of explanatory variables were used for building the primary (MLR) model. The model was tested through a set of statistical tests to ensure its appropriateness for the data. From the results, the study reveals that year of study, family financial level, gender and school are significant factors in explaining spending habits of students. Despite the fact that gender is one of the deterministic factors of financial behavior of student, this model identified family level of income as a major deterministic factor. Conclusively, using MLR model accurately defines the relationship between the group of explanatory variables and the response variable. It also identifies the effect of each of the variables, and we can predict the classification of any individual case. The researchers recommend that, the Universities peer counselling department, should hold trainings on the basis of major determinant of financial spending behavior i.e. family financial level.
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    Review of Methods of Estimating Parameters In Nonlinear Mixed-Effects (Nlme) Models
    (University of Nairobi, 2007-07) Mbunzi, Stephen M.
    This study is a critical review of theoreticalissues that underline the linear mixed effects (LME) and nonlinear mixed effects (NLME) models. These two areas are revisited under maximum likelihood and restricted maximumlikelihood estimation frameworks. We also review methods of estimating parameters in both linear and nonlinear mixed effects models. In the case of LME, we consider different ways of developing the likelihood estimators, key among these methods are the “pseudo-data” approach, orthogonal triangular decomposition method and the use of penalized least squares problem. For NLME, we intended to investigate the computational efficiency and accuracy of computational methods, like the b-splines, that could be used to approximate the log-likelihood function in non-linear mixed effects models. This was not achieved in this study but can be an interesting area for further research work. We critically review the four methods of estimating parameters by Pinheiro and Bates (1995) through proving a number of lemmas. Our proves led us to same stated results by different researchers in different papers. This is a key issue in the investigation of other expansion methods and comparing their computational efficiency and accuracy with these existing ones. We conclude by giving an insight into linear mixed effects models by analyzing a data set from livestock where we examine incorporation of random effects to study variations among rams (sires) and ewes (dams) and their influences on lamb weaning weight. Factors like year of birth of the lamb, sex of lamb, age at weaning, age of dam, ewe breed and ram breed are found to influence the weaning weight differently. With the random terms (ewes and rams) specified in the model the estimate of the residual among lamb variance is found to reduce due to taking into account the variations among rams and ewes within breeds. It was our intention to obtain heritability estimates which determine the proportion of the variation among offspring that have been handed down from parents out of these random estimates.
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    Staff Profile - Stephen Muteti Mbunzi
    (University of Embu, 2015) Mbunzi, Stephen M.
    Currently studying PhD in JKUAT, in the Department of Statistics and Actuarial Sciences, on Applied Statistics on Modeling Jigger Infestation using Image Analysis. I did my MSc. (Statistics) in the University of Nairobi from 2005 to 2007 and published one paper in 2009. I’m married to one wife and blessed with three children.