-
Spatial Econometric Model of Poverty in Java Island
Mulugeta Aklilu Zewdie,
M. Nur Aidi,
Bagus Sartono
Issue:
Volume 4, Issue 6, November 2015
Pages:
420-425
Received:
26 August 2015
Accepted:
15 September 2015
Published:
26 September 2015
Abstract: This paper gives the concept of spatial econometric model and applies it to analyze the spatial dimensions of poverty and its determinants using data from Java Island 2010 census survey, for 105 districts of Java Island. Dependent variable used in this research is percentage of poverty rate at particular district and predictors are some selected variables that are correlated to poverty. Weighted matrix is obtained by using queen contiguity criteria and four statistical models are applied to the data, Ordinary Least Square regression model, Spatial Error Model, Spatial Lag Model and Spatial Durbin Model. It is shown that the OLS estimates of the poverty function suffer from spatial effects that indicated the OLS model are miss specified since Moran Index test also confirmed the existence of spatial autocorrelation. LM and Robust LM are used for testing the existence of spatial effect. The Likelihood Ratio common factor test and AIC are used for model selection criteria. Gauss Markov Assumptions are done and the Spatial Lag model proved to be better than other model for a given data and the result shows that Education and Working hours has significant impact on poverty.
Abstract: This paper gives the concept of spatial econometric model and applies it to analyze the spatial dimensions of poverty and its determinants using data from Java Island 2010 census survey, for 105 districts of Java Island. Dependent variable used in this research is percentage of poverty rate at particular district and predictors are some selected va...
Show More
-
Modelling the Volatility of Exchange Rates in Rwandese Markets
Jean de Dieu Ntawihebasenga,
Joseph Kyalor Mung’atu,
Peter Nyamuhanga Mwita
Issue:
Volume 4, Issue 6, November 2015
Pages:
426-431
Received:
15 August 2014
Accepted:
19 March 2015
Published:
25 September 2015
Abstract: This work applied Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approachto modelling volatility in Rwanda Exchange rate returns. The Autoregressive (AR) model with GARCH errors was fitted to the daily exchange rate returns using Quasi-Maximum Likelihood Estimation (Q-MLE) method to get the current volatility, asymptotic consistency and asymptotic normality of estimated parameters.Akaike Information criterion was used for appropriate GARCH model selection while Jarque Bera test used for normality testing revealed that both returns and residuals have fat tails behaviour. It was shown that the estimated model fits Rwanda exchange rate returns data well.
Abstract: This work applied Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approachto modelling volatility in Rwanda Exchange rate returns. The Autoregressive (AR) model with GARCH errors was fitted to the daily exchange rate returns using Quasi-Maximum Likelihood Estimation (Q-MLE) method to get the current volatility, asymptotic consiste...
Show More
-
Hotelling’s T2 Decomposition: Approach for Five Process Characteristics in a Multivariate Statistical Process Control
Adepoju Ajibola Akeem,
Abubakar Yahaya,
Osebekwin Asiribo
Issue:
Volume 4, Issue 6, November 2015
Pages:
432-437
Received:
27 August 2015
Accepted:
13 September 2015
Published:
28 September 2015
Abstract: Multivariate statistical process control (MSPC) is the most acceptable monitoring tool for several variables, and it is advantageous when compare to the simultaneous use of univariate scheme. However, there are some disadvantages in this scheme which include identification of influential variable(s). The Mason, Young and Tracy (MYT) decomposition diagnosis is one of the approaches commonly use to identify the influential variables. This approach aid the breaking down, the overall T square value and show the individual variable contribution, while their joint contributions is also revealed. The challenges of this approach include rigorous derivation of model, computation and complexity more especially when the size of the process characteristics is large. In this research paper we extend the decomposition derivation to five variables. One hundred and twenty (120) models (decomposition partitions) are obtained from the decomposition, revealing the invariance property of the Hotelling’s T square statistic, and eighty (80) unique terms.
Abstract: Multivariate statistical process control (MSPC) is the most acceptable monitoring tool for several variables, and it is advantageous when compare to the simultaneous use of univariate scheme. However, there are some disadvantages in this scheme which include identification of influential variable(s). The Mason, Young and Tracy (MYT) decomposition d...
Show More
-
Socio-Economic Determinants of Low Birth Weight in Kenya: An Application of Logistic Regression Model
Issue:
Volume 4, Issue 6, November 2015
Pages:
438-445
Received:
10 September 2015
Accepted:
21 September 2015
Published:
12 October 2015
Abstract: Babies born with Low-birth weight are at increased risk for serious health problems which are accompanied by disabilities and even death. The purpose of this study was to determine socio-economic factors that lead to low birth weight of children in Kenya. Data used was from Kdhs 2003 and the significant effect of socio-economic determinants on low birth weight was examined using logistic regression analysis data is categorical and continuous in nature, where predictor variables being socio-economic determinants and birth weight being dependent variable. Results indicate that out of six socio-economic factors involved in the study, four (Religion, Time Wanted Pregnancy, Marital Status and Economic Status) revealed some significant effects on the children with low birth weight. Therefore Socio-economic determinants have a significant effect on Low birth weight which suggests a strong negative associated with infant survival in Kenya independent of other risk factors. The logistic function revealed a statistically significant association between the birth weight, Religion, Time Wanted Pregnancy, Marital Status and Economic Status. Predicted probability is 11.4% low birth weight. Researcher recommends that respondents should avoid conceiving unexpectedly since it was associated with high low birth weight. Also to effectively enhance normal birth weight in Kenya, then expectant mothers should keenly focus on the socio-economic determinants by avoiding marital problems like divorce.
Abstract: Babies born with Low-birth weight are at increased risk for serious health problems which are accompanied by disabilities and even death. The purpose of this study was to determine socio-economic factors that lead to low birth weight of children in Kenya. Data used was from Kdhs 2003 and the significant effect of socio-economic determinants on low ...
Show More
-
An Assessment of Farmers Livelihood in the Coffee Certification Schemes in Tanzania
Issue:
Volume 4, Issue 6, November 2015
Pages:
446-463
Received:
14 September 2015
Accepted:
26 September 2015
Published:
12 October 2015
Abstract: This study was undertaken to assess the impacts of adoption of various types of coffee certifications on the livelihoods of smallholder farmers. The main objective of this study was to compare the livelihood of farmers under the different producer groups with respect to their income and food security situation. It begins with an introduction to impact assessment and a description of the methodology and its challenges with an outline of the method used for handling outliers and comparing the certified and non-certified farmers and the producer groups. Secondary data from coffee survey data collected by COSA and partners for analyzing the impact of sustainability standards forms the basis of this study. Multi stage cluster sampling was used to sample farmers that were interviewed. In the first stage, the coffee growing areas in Tanzania and the active certification programs were identified. Then second level producer groups that had obtained certification were used to obtained the sampling frame of the first level producer groups. Random sampling was then used to select the first level producer groups and also randomly select villages with farmer in the producer groups. Non parametric methods have been used to compare the producer groups because one sample does not follow a normal distribution and most of them are highly skewed. Error bars plots have been used to compare the significance difference in the producer groups. Aggregate income from the different forms in which coffee was sold has been computed and used for comparison. It also evaluates the food security situation last production year of the farmers across the different producer groups. The key indicators used, showed that generally, adoption of the various coffee certifications programs have positive impacts on income and food security. In the course of this study, the areas of further research that emerged are; an evaluation of the farmers livelihood before intervention is done to ascertain whether their livelihood has changed due to adoption of certification or due to other factors and the development of a stepwise procedure for an outlier identification and ascertaining their validity. The methods that were used for outlier detection were subjective.
Abstract: This study was undertaken to assess the impacts of adoption of various types of coffee certifications on the livelihoods of smallholder farmers. The main objective of this study was to compare the livelihood of farmers under the different producer groups with respect to their income and food security situation. It begins with an introduction to imp...
Show More
-
Investigation of over the Counter Diagnosis and Drug Dispensation in Chemists: A Case Study in Thika Sub-County, Kenya
Issue:
Volume 4, Issue 6, November 2015
Pages:
464-470
Received:
22 September 2015
Accepted:
25 September 2015
Published:
12 October 2015
Abstract: Over-the-counter (OTC) drugs are medicines that may be obtained directly by a consumer without a prescription from a healthcare professional, as compared to drugs sold to consumers possessing a valid prescription. In many countries, Kenya included, these drugs are often located on the shelves of stores like any other packaged product. Some drugs may be legally classified as OTC but may only be dispensed by a pharmacist after an assessment of the patient's needs and/or the provision of patient education. OTC drugs are capable of being misused, abused especially where inappropriate drugs and incorrect dosages are given which may lead to short and long-term negative effects. The major concern surrounds the correct diagnosis and the appropriateness of the dispensed drugs and information provided to the consumers. This study focused on the OTC drugs in chemists. It was important to know why people opt for OTC drugs instead of the prescribed drugs. To meet this objective, an observational study was carried out in Thika Sub-county of Kenya to determine why patients prefer the OTC drugs to prescribed drugs. The results showed that the cost of prescription, source of diagnosis information, source of prescription information, amount of income of the respondent and previous experience on the same similar symptoms were determinants of buying OTC drugs. Education levels, age, place of residence, occupation and hospital type near the respondent were the covariates. The results of this study have enabled the researcher to come up with recommendations to the Ministries of Medical Services and that of Public Health on the best policies to use in dispensing OTC drugs.
Abstract: Over-the-counter (OTC) drugs are medicines that may be obtained directly by a consumer without a prescription from a healthcare professional, as compared to drugs sold to consumers possessing a valid prescription. In many countries, Kenya included, these drugs are often located on the shelves of stores like any other packaged product. Some drugs ma...
Show More
-
Extended Cox Modeling of Customer Retention in Mobile Telecommunication Sector of Rwanda
Diane Ingabire,
Samuel Musili Mwalili,
George Otieno Orwa
Issue:
Volume 4, Issue 6, November 2015
Pages:
471-479
Received:
25 August 2015
Accepted:
15 September 2015
Published:
13 October 2015
Abstract: Retaining customers improves profitability, importantly reduces the cost incurred in acquiring new customers and moreover a firm can increase profits by 25-95 percent if it could improve its customer retention rates by 5 percent. As markets mature and competitive pressure intensifies, companies can no longer ignore the importance of customer retention as their existing customer bases have become their precious assets. This research aims to model customer retention in Rwandan telecom sector using survival analysis technique in order to inform the concerned institutions and companies about telecom customer retention in Rwanda. The Cox regression model and extended Cox model were developed using simulation approach in order to assess which model is the best for customer retention. It was found that the customer’s socio-economic, demographic and behavioral characteristics have an effect on churn rate. The extended Cox model was the best description of how customer retention is achieved. These findings hold implications for industry operators on key areas to pay attention to in order to achieve customer retention.
Abstract: Retaining customers improves profitability, importantly reduces the cost incurred in acquiring new customers and moreover a firm can increase profits by 25-95 percent if it could improve its customer retention rates by 5 percent. As markets mature and competitive pressure intensifies, companies can no longer ignore the importance of customer retent...
Show More
-
Application of Homotopy Perturbation and Sumudu Transform Method for Solving Burgers Equations
Amjad Ezoo Hamza,
Tarig M. Elzaki
Issue:
Volume 4, Issue 6, November 2015
Pages:
480-483
Received:
16 September 2015
Accepted:
23 September 2015
Published:
14 October 2015
Abstract: In this paper, the exact solution of Burgers equations are obtained by using coupling homotopy perturbation and Sumudu transform method (HPSTM), theoretical considerations are discussed, to illustrate the capability and reliability some examples are provided, the results reveal that method is very effective and simple.
-
Use of Exponential Smoothing Technique in Estimation of Returns in a Financial Portfolio (A Case of the Matatu Public Transport Business in Kenya)
Jumba Minyoso Sandra,
Joel Cheruiyot Chelule,
Mungatu Joseph
Issue:
Volume 4, Issue 6, November 2015
Pages:
484-495
Received:
17 September 2015
Accepted:
7 October 2015
Published:
22 October 2015
Abstract: This study sought to develop consistent estimators for the conditional mean and conditional volatility using exponential smoothing technique and to use the estimators for the conditional mean and conditional volatility to estimate VaR and ES of a financial asset in a given financial portfolio. In particular, we take the Kenyan Matatu business as our financial portfolio and we estimate the ES of the daily returns obtained from Matatus travelling the Nairobi –Eldoret highway as provided by CLASSIC SACCO. In estimating the conditional mean and conditional volatility of the returns of our portfolio, the study explored the exponential smoothing technique, whereby exponentially decreasing weights were assigned to the returns. The study proved that the estimators for the conditional mean and conditional volatility are consistent and also that the estimators for the conditional mean and conditional volatility when conditional mean is known, are asymptotically normal. Further the study gives the estimators for the VaR and ES and proves that the VaR estimator is consistent.
Abstract: This study sought to develop consistent estimators for the conditional mean and conditional volatility using exponential smoothing technique and to use the estimators for the conditional mean and conditional volatility to estimate VaR and ES of a financial asset in a given financial portfolio. In particular, we take the Kenyan Matatu business as ou...
Show More
-
Modelling Crime Rate Using a Mixed Effects Regression Model
Chris Muchwanju,
Joel Cheruyiot Chelule,
Joseph Mung’atu
Issue:
Volume 4, Issue 6, November 2015
Pages:
496-503
Received:
2 October 2015
Accepted:
15 October 2015
Published:
28 October 2015
Abstract: In this paper we propose a type of Mixed effects Regression Model, that is Hierarchical Linear Model to study crime rate. We derive the estimators of the proposed model and discuss the asymptotic properties of the model. In order to test for the practicability of the proposed model, we estimate a crime equation using a panel dataset of the provinces in Kenya for the period 1992 to 2012 employing the REML estimator. Our empirical results suggest that Poverty Rate, Unemployment rate, Probability of arrest, population Density and police rate are correlated to all typologies of crime rate considered. The results further suggest that crime rate is better explained at provincial level as compared to country level.
Abstract: In this paper we propose a type of Mixed effects Regression Model, that is Hierarchical Linear Model to study crime rate. We derive the estimators of the proposed model and discuss the asymptotic properties of the model. In order to test for the practicability of the proposed model, we estimate a crime equation using a panel dataset of the province...
Show More
-
Modeling Survival Data by Using Cox Regression Model
Medhat Mohamed Ahmed Abdelaal,
Sally Hossam Eldin Ahmed Zakria
Issue:
Volume 4, Issue 6, November 2015
Pages:
504-512
Received:
10 September 2015
Accepted:
30 September 2015
Published:
30 October 2015
Abstract: Survival analysis refers to the general set of statistical methods developed specifically to model the timing of events. A popular regression model for the analysis of survival data is the Cox proportional hazards regression model. The Cox regression model is a semi parametric model, making fewer assumptions than typical parametric methods but more assumptions than those nonparametric methods. The main objective of this paper is to construct Cox proportional hazards regression model for examining the covariate effects on the hazard function and to determine the risk factors affecting the outcome of liver transplantation operation for end-stage liver disease. This article will focus on a review of (a) the Cox model and interpretation of its results, (b) assessment of the validity of the PH assumption, and (c) accommodating non-proportional hazards using covariate stratification. Cox PH model showed that the variables: Recipient age, 〖MELD〗_3 Score, Ln_Creatinine, and GRWR are statistically significant and selected as significant factors for risk of death after liver transplantation operation. Also the scaled Schoenfeld residual displayed non-proportionality for variable Recipient Age and this variable needed to be stratified. And the Cox-Snell residual showed the Cox PH model does not fit these data adequately. So the stratified Cox model could be more appropriate to the current study. The stratified Cox model with interaction and with no interaction were applied and showed that the no-interaction model is acceptable at 0.05 level of significance and the variables〖MELD〗_3 Score, Ln_Creatinine are statistically significant and selected as significant factors for risk of death after liver transplantation operation at 0.05 level of significance.
Abstract: Survival analysis refers to the general set of statistical methods developed specifically to model the timing of events. A popular regression model for the analysis of survival data is the Cox proportional hazards regression model. The Cox regression model is a semi parametric model, making fewer assumptions than typical parametric methods but more...
Show More
-
Modeling Adoption and Usage of Mobile Money Transfer Services in Kenya Using Structural Equations
Joseph Kuria Waitara,
Anthony Gichuhi Waititu,
Anthony Kibera Wanjoya
Issue:
Volume 4, Issue 6, November 2015
Pages:
513-526
Received:
24 September 2015
Accepted:
21 October 2015
Published:
30 October 2015
Abstract: Mobile Money Transfer services have eased the means of transferring money from one mobile phone user to another in Kenya. Since the introduction of the services there is disparity in adoption of different mobile money transfer platforms in Kenya. In this study Structural Equation Modeling was used to create a model of factors that influence the adoption and usage of Mobile Money Transfer services in Kenya. The findings in this study provide useful information to Mobile Network Operators that they can use in implementation of their Mobile Money Transfer service. The study was conducted in Juja Township. The study established that the independent variable namely, Performance Expectancy, Effort Expectancy and Social Influence had significant influence on Behavioral Intention towards the use of a given Mobile Money Transfer service. This means that the MMT’s users would continue to use a given Mobile Money Transfer service they have chosen. Facilitating Conditions was found to be a significant factor in predicting adoption and use of Mobile Money Transfer for males and females where gender was used as moderating factor. Also Behavioral intention was a significant determinant of Use Behavior of Mobile Money Transfer services. In conclusion the research model was found to be important in determining factors that influence the adoption and use of a given Mobile Money Transfer service.
Abstract: Mobile Money Transfer services have eased the means of transferring money from one mobile phone user to another in Kenya. Since the introduction of the services there is disparity in adoption of different mobile money transfer platforms in Kenya. In this study Structural Equation Modeling was used to create a model of factors that influence the ado...
Show More
-
Modelling a Pay-As-You-Drive Insurance Pricing Structure Using a Generalized Linear Model: Case Study of a Company in Kiambu
Charity Mkajuma Wamwea,
Benjamin Kyalo Muema,
Joseph Kyalo Mung’atu
Issue:
Volume 4, Issue 6, November 2015
Pages:
527-533
Received:
28 September 2015
Accepted:
15 October 2015
Published:
30 October 2015
Abstract: The current fixed car-year pricing of auto insurance is inefficient and actuarially inaccurate since motorists in the same risk class pay the same amount of premium regardless of the number of miles covered by the different vehicles. In this paper, a simple alternative, the pay as you drive insurance, was proposed whereby motorists only pay for the mileage covered by their vehicles. The main objective was to find a suitable probability distribution that would be used to model the per kilometer risk premiums for the total aggregate claims cost. A case study was done for a company in Kiambu county. The data collected consisted of 5 variables in 194 categories whereby the total aggregate claims cost was the dependent variable. The data collection technique was via a census. The most appropriate model was found to be the zero inflated negative binomial model. The significant factors were found to be the make of the vehicle, annual mileage, and present value of the vehicle. In addition to this, mileage was also found to be positively correlated to the total aggregate claims cost.
Abstract: The current fixed car-year pricing of auto insurance is inefficient and actuarially inaccurate since motorists in the same risk class pay the same amount of premium regardless of the number of miles covered by the different vehicles. In this paper, a simple alternative, the pay as you drive insurance, was proposed whereby motorists only pay for the...
Show More
-
Solution of Linear and Nonlinear Schrodinger Equations by Combine Elzaki Transform and Homotopy Perturbation Method
Mohannad H. Eljaily,
Tarig M. Elzaki
Issue:
Volume 4, Issue 6, November 2015
Pages:
534-538
Received:
29 September 2015
Accepted:
15 October 2015
Published:
30 October 2015
Abstract: In this paper, the homotopy perturbation method (HPM) and ELzaki transform are employed to obtain the approximate analytical solution of the Linear and Nonlinear Schrodinger Equations. The proposed method is an elegant combination of the new integral transform “ELzaki Transform” and the homotopy perturbation method. This method finds the solution without any discretization, linearization or restrictive assumptions and avoids the round-off errors,the results reveal that the ETHPM is very efficient, simple and can be applied to other nonlinear problems.
Abstract: In this paper, the homotopy perturbation method (HPM) and ELzaki transform are employed to obtain the approximate analytical solution of the Linear and Nonlinear Schrodinger Equations. The proposed method is an elegant combination of the new integral transform “ELzaki Transform” and the homotopy perturbation method. This method finds the solution w...
Show More
-
Modelling Oil Price Risk
Mwelu Susan,
Anthony Gichuhi Waititu
Issue:
Volume 4, Issue 6, November 2015
Pages:
539-546
Received:
5 October 2015
Accepted:
23 October 2015
Published:
13 November 2015
Abstract: The energy sector is regarded as a key driving force for all other sectors in the economy. This can be attributed to oil being the global main source of energy as well as oil prices having a significant impact on financial markets and world economies. With the emergence of relatively free oil markets, prices are vulnerable to high shifts resulting in increased exposure to price risk. This research project focuses on the oil markets with two main oil price benchmarks being used: Brent blend of Europe and WTI of United States of America. As opposed to estimating a single distribution for the entire return series generating process this research project focuses on the tails of the distributions using limit laws from the Extreme Value Theory. A two stage GARCH-EVT approach is preferred in the study. The focus is on the peak over threshold method for analysing the generalized Pareto distributed exceedances over some significantly high threshold. The results of this study reveal that oil prices are highly volatile, heteroscedastic and fat-tailed. In addition the GPD fits the tails adequately well and is used to estimate associated tail risks at sufficiently high probabilities.
Abstract: The energy sector is regarded as a key driving force for all other sectors in the economy. This can be attributed to oil being the global main source of energy as well as oil prices having a significant impact on financial markets and world economies. With the emergence of relatively free oil markets, prices are vulnerable to high shifts resulting ...
Show More
-
Statistical Analysis of Urban Quality of Life (Case Study: Hawassa Town, SNNP Region, Ethiopia)
Natnael Mamuye,
Bute Gotu
Issue:
Volume 4, Issue 6, November 2015
Pages:
547-554
Received:
14 October 2015
Accepted:
26 October 2015
Published:
19 November 2015
Abstract: The study on Quality of life in the cities of both developing and developed countries is gaining interest from a variety of disciplines and is becoming an important tool for policy evaluation, rating of cities, urban planning and management. Cities are the center of economy, politics, commerce and other activities, so it is necessary to analyze the conditions that contribute to the quality of urban life. This study is on urban quality of life of the residents in Hawassa city and its main purpose is to identify the factors that may affect the quality of life of Hawassa residents. For the study a cross sectional data from 570 heads of household which were selected based on stratified random sampling by making the seven sub cities in Hawassa as stratum was collected. Statistical methods such as descriptive statistics, factor analysis and binary logistic regression are used to analyze the data in the study. The principal component analysis revealed that six factors (dimensions) of quality of life were extracted from twenty subjective attributes and all of the factor scores are positively and significantly related to quality of life. Factor analysis also extracts six factors using fifteen objective attributes. Housing, length of residence, economic status, distance from educational center and religious place all have statistically significant impact on people’s quality of life in Hawassa. But access to public service is not significant predictor of quality of life of the residents in Hawassa. Housing, economic condition, environment, neighborhood safety and security, social connectedness and quality of public service are identified as dimensions of subjective quality of life of the residents in Hawassa. The paper also conclude that socio-economic affairs, access to public service, access to education, housing, access to religious place and length of residency are found to be the dimensions of the objective quality of life of the residents in Hawassa.
Abstract: The study on Quality of life in the cities of both developing and developed countries is gaining interest from a variety of disciplines and is becoming an important tool for policy evaluation, rating of cities, urban planning and management. Cities are the center of economy, politics, commerce and other activities, so it is necessary to analyze the...
Show More
-
A Bivariate Probit Model for Correlated Binary Data with Application to HIV and Male Circumcision
Tabitha Wambui Njoroge,
Samuel Musili Mwalili,
Anthony Kibira Wanjoya
Issue:
Volume 4, Issue 6, November 2015
Pages:
555-561
Received:
29 October 2015
Accepted:
9 November 2015
Published:
19 November 2015
Abstract: As the HIV/AIDS epidemic continues to grow, it continues to be a huge threat to the social and economic well-being of a society. Studies show that the epidemic has significantly affected the development of Kenya. Numerous interventions by different bodies (e.g. the national government, international donors, civil society organizations) to prevent its spread continue to be put in place. Male Circumcision has been proven to reduce the risk of HIV transmission. A statistical model that shows the relationship between male circumcision and HIV prevalence is therefore of great importance as it can be used to bring out the inverse relationship between the two response variables and hence support male circumcision as an effective intervention for prevention of HIV spread. We use Bivariate Probit regression to model the correlation between Male Circumcision and HIV prevalence while looking into factors affecting both HIV and Male Circumcision.
Abstract: As the HIV/AIDS epidemic continues to grow, it continues to be a huge threat to the social and economic well-being of a society. Studies show that the epidemic has significantly affected the development of Kenya. Numerous interventions by different bodies (e.g. the national government, international donors, civil society organizations) to prevent i...
Show More
-
Statistical Analysis on the Loan Repayment Efficiency and Its Impact on the Borrowers: a Case Study of Hawassa City, Ethiopia
Yonas Shuke Kitawa,
Nigatu Degu Terye
Issue:
Volume 4, Issue 6, November 2015
Pages:
562-575
Received:
10 September 2015
Accepted:
7 October 2015
Published:
26 November 2015
Abstract: The major objective of this study is to study factors affecting loan repayment efficiency of borrowers and assess impact of efficient utilization of loan for the borrowers in Hawassa city in Ethiopia. Data used for this study was collected through a structured questionnaire. Classical and Bayesian logistic regression technique were used for data analysis. Factor analysis was used to reduce data and to incorporate the major determinants that the efficient utilization of loan have to the borrowers, whereas logistic regression is used to obtained factors affecting loan repayment performance of borrowers and it was extended to the Bayesian frame work using prior information that the parameter follows. Results of the classical binary logistic regression indicate that better repayment efficiency is associated with borrowers: sex, educational status, number of dependent family member, monthly income, loan size, additional source of income, motivation of repayment, time given for repayment, interest rate and screening mechanism when individuals apply for the loan. Also by using Bayesian logistic regression age, loan type, using loan for intended purpose and experience are significant in addition to significant predictors in classical logistic regression. From factor analysis, 27 factor used for impact assessment in which all the factor loaded highly in 7 significant factors like:-Benefit and obstacle related factor, capital effect, saving habit, expenditure, government spending, satisfaction level on the service and consumption change that has been seen after using loan. Thus, in order to improve repayment performance of borrowers, increasing loan size, training and giving some incentive in business areas, increasing awareness in different ways and studying factors which has significant impact on borrowers creditworthiness and giving solution to reduce that problems must be improved.
Abstract: The major objective of this study is to study factors affecting loan repayment efficiency of borrowers and assess impact of efficient utilization of loan for the borrowers in Hawassa city in Ethiopia. Data used for this study was collected through a structured questionnaire. Classical and Bayesian logistic regression technique were used for data an...
Show More
-
Probability Model for Human Fertility Behavior: Straddling Birth Interval Under Realistic Assumptions
Issue:
Volume 4, Issue 6, November 2015
Pages:
576-580
Received:
31 October 2015
Accepted:
9 November 2015
Published:
26 November 2015
Abstract: Fertility analysis is important in understanding past, current and future trends of population size, Composition and growth. Information on fertility levels, patterns and trends experienced by a country is important for socio-economic planning, monitoring and evaluating programs. In recent years the study of birth intervals has acquired importance because of its relationships to fertility. The data on straddling birth interval, defined as a closed birth interval that straddles the survey date, is easy to obtain more accurately, though the collection of data requires retrospective as well as prospective surveys. This type of interval is useful for the study of reproduction of subsequent fecund women of a particular age group. In this paper, a probability distribution for the straddling birth interval regardless of parity has been derived by taking into account that different proportion of females are exposed to the risk of conception at different point of time. In this derived model, fecundability (λ) has been considered to be constant over the study period. The duration of time from the point of termination of PPA to the state of exposure has been taken as random variable (µ) which follows exponential distribution. The maximum likelihood estimation technique has been used for the estimation of parameter (λ) & (µ) through derived model.
Abstract: Fertility analysis is important in understanding past, current and future trends of population size, Composition and growth. Information on fertility levels, patterns and trends experienced by a country is important for socio-economic planning, monitoring and evaluating programs. In recent years the study of birth intervals has acquired importance ...
Show More
-
The Power of the Pruned Exact Linear Time(PELT) Test in Multiple Changepoint Detection
Gachomo Dorcas Wambui,
Gichuhi Anthony Waititu,
Anthony Wanjoya
Issue:
Volume 4, Issue 6, November 2015
Pages:
581-586
Received:
5 November 2015
Accepted:
11 November 2015
Published:
2 December 2015
Abstract: Changepoint detection is the problem of estimating the point at which the statistical properties of a sequence of observations change. Over the years several multiple changepoint search algorithms have been proposed to overcome this challenge. They include binary segmentation algorithm, the Segment neighbourhood algorithm and the Pruned Exact Linear Time (PELT) algorithm. The PELT algorithm is exact and under mild conditions has a computational cost that is linear in the number of data points. PELT is more accurate than binary segmentation and faster as than other exact search methods. However, there is scanty literature on the sensitivity/power of PELT algorithm as the changepoints approach the extremes and as the size of change increases. In this paper, we implemented the PELT algorithm which uses a common approach of detecting changepoints through minimising a cost function over possible numbers and locations of changepoints. The study used simulated data to determine the power of the PELT test. The study investigated the power of the PELT algorithm in relation to the size of the change and the location of changepoints. It was observed that the power of the test, for a given size of change, is almost the same at all changepoints location. Also, the power of the test increases with the increase in size of change.
Abstract: Changepoint detection is the problem of estimating the point at which the statistical properties of a sequence of observations change. Over the years several multiple changepoint search algorithms have been proposed to overcome this challenge. They include binary segmentation algorithm, the Segment neighbourhood algorithm and the Pruned Exact Linea...
Show More
-
Multivariate Analysis Approach on the Study of Quality of Life: A Case Study in Some Towns of Amhara Regional State, Ethiopia
Genanew Timerga Neri,
Nigatu Degu Terye,
Haymanot Zeleke Tadesse,
Woldesadik Kagnew Abebaw,
Tena Manaye Endalamaw
Issue:
Volume 4, Issue 6, November 2015
Pages:
587-601
Received:
28 October 2015
Accepted:
9 November 2015
Published:
8 December 2015
Abstract: Quality of life (QOL) is gaining interest from a variety of disciplines and important tool for policy evaluation, rating of cities, urban planning and management. Cities are the center of economy, politics, commerce and other activities, so very necessary to analyze the conditions that contribute to the quality of urban life. This study identifies the factors that affect QOL of the people in the region. 809 household heads were selected based on stratified random sampling method. Different statistical methods have been used to analyze the primary data. Factor analysis is used to reduce the number of dimensions of both subjective and objective quality of life into few, which are unrelated to each other. Binary logistic regressions and ordinal logistic regressions are also applied to identify the most significant factors that can affect quality of life in the region. The principal component analysis revealed that 6 dimensions of QOL were extracted from 20 subjective attributes; namely; housing, economic, environmental, neighborhood safety and security, social connectedness and quality of public service. Binary logistic regression model shows all of the dimensions are significantly related to QOL. Factor analysis extract 6 factors using 15 objective attributes, namely; socio-economic, access to public service, access to education, housing, religion and length of residency are found to significant predictor of QOL in objective dimensions of the region. Religion and length of residence have positive impact and other have negative contribution to QOL. Results of this study can be used in designing future urban QOL studies in the region.
Abstract: Quality of life (QOL) is gaining interest from a variety of disciplines and important tool for policy evaluation, rating of cities, urban planning and management. Cities are the center of economy, politics, commerce and other activities, so very necessary to analyze the conditions that contribute to the quality of urban life. This study identifies ...
Show More
-
Discriminant Analysis Procedures Under Non-optimal Conditions for Binary Variables
Issue:
Volume 4, Issue 6, November 2015
Pages:
602-609
Received:
12 October 2015
Accepted:
26 October 2015
Published:
10 December 2015
Abstract: The performance of four discriminant analysis procedures for the classification of observations from unknown populations was examined by Monte Carlo methods. The procedures examined were the Fisher Linear discriminant function, the quadratic discriminant function, a polynomial discriminant function and A-B linear procedure designed for use in situations where covariance matrices are equal. Each procedure was observed under conditions of equal sample sizes, equal covariance matrices, and in conditions where the sample was drawn from populations that have a multivariate normal distribution. When the population covariance matrices were equal, or not greatly different, the quadratic discriminant function performed similarly or marginally the same like Linear procedures. In all cases the polynomial discriminate function demonstrated the poorest, linear discriminant function performed much better than the other procedures. All of the procedures were greatly affected by non-normality and tended to make many more errors in the classification of one group than the other, suggesting that data be standardized when non-normality is suspected.
Abstract: The performance of four discriminant analysis procedures for the classification of observations from unknown populations was examined by Monte Carlo methods. The procedures examined were the Fisher Linear discriminant function, the quadratic discriminant function, a polynomial discriminant function and A-B linear procedure designed for use in situa...
Show More
-
Analysis of Progressive First-Failure-Censoring for Non-normal Model Using Competing Risks Data
A. A. Modhesh,
G. A. Abd-Elmougod
Issue:
Volume 4, Issue 6, November 2015
Pages:
610-618
Received:
8 September 2015
Accepted:
7 October 2015
Published:
14 December 2015
Abstract: Competing risks data usually arises in studies in which the death or failure of an individual or an item may be classified into one of T≥2 mutually exclusive causes. In this paper, we will study the competing risks model when the data is progressively first-failure-censored. Based on this type of censoring, we derive the maximum likelihood estimators (MLE's) for the unknown parameters. Approximate confidence intervals and two bootstrap confidence intervals are also proposed. The results in the cases of first-failure censoring, progressive Type II censoring, Type II censoring and complete sample are special cases. A real data set has been analyzed for illustrative purposes. Different methods have been compared using Monte Carlo simulations.
Abstract: Competing risks data usually arises in studies in which the death or failure of an individual or an item may be classified into one of T≥2 mutually exclusive causes. In this paper, we will study the competing risks model when the data is progressively first-failure-censored. Based on this type of censoring, we derive the maximum likelihood estimato...
Show More
-
Estimation of Proportion of a Trait by Batch Testing Model in a Quality Control Process
Ronald W. Wanyonyi,
Kennedy L. Nyongesa,
Adu Wasike
Issue:
Volume 4, Issue 6, November 2015
Pages:
619-629
Received:
7 November 2015
Accepted:
27 November 2015
Published:
22 December 2015
Abstract: Batch testing involves testing items in a group rather than testing the items individually for resource saving purposes. Estimation of proportion of a trait of interest using batch testing model insulates individuals of a population against stigma. In this paper, an estimator of the unknown proportion of a trait in batch testing model based on a quality control process is constructed and its properties discussed. In quality control, a batch is rejected if constituent defective members are greater than l, the cut off value. It is observed that if l = 0, then the obvious batch testing strategy is obtained. Hence when l > 0, the batch testing strategy is generalized. The proposed model is superior to the existing models when the proportion of a trait is relatively high. The application of the model on Genetically Modified Organisms contamination is carried out.
Abstract: Batch testing involves testing items in a group rather than testing the items individually for resource saving purposes. Estimation of proportion of a trait of interest using batch testing model insulates individuals of a population against stigma. In this paper, an estimator of the unknown proportion of a trait in batch testing model based on a qu...
Show More
-
Significance Test in Meta-analysis Approach: A theoretical Review
Issue:
Volume 4, Issue 6, November 2015
Pages:
630-639
Received:
7 December 2015
Accepted:
21 December 2015
Published:
8 January 2016
Abstract: Meta-analysis, a statistical procedure that integrates the results of several independent studies, plays a central role in statistical research, and a very important task in research problems and statistical significance tests. This paper discusses these principles, along with the practical steps in performing meta-analysis. It describes the issue of meta-analysis, explains what meta-analysis is, how it is done and how it can be interpreted. Some related problems such as statistical significance, effect size and power analysis are described. Examples of implementation on theoretical data would be carried. Results, conclusions, recommendations on the use of meta-analysis would be summarized.
Abstract: Meta-analysis, a statistical procedure that integrates the results of several independent studies, plays a central role in statistical research, and a very important task in research problems and statistical significance tests. This paper discusses these principles, along with the practical steps in performing meta-analysis. It describes the issue ...
Show More
-
Detection of Multicollinearity Using Min-Max and Point-Coordinates Approach
Umeh Edith Uzoma,
Awopeju Kabir Abidemi,
Ajibade F. Bright
Issue:
Volume 4, Issue 6, November 2015
Pages:
640-643
Received:
14 December 2015
Accepted:
23 December 2015
Published:
23 January 2016
Abstract: Multicollinearity is one of the problems or challenges of modeling or multiple regression usually encountered by Economists and Statisticians. It is a situation where by some of the independent variables in the formulated model are significantly or highly related/correlated. In the past, methods such as Variance Inflation Factor, Eigenvalue and Product moment correlation have been used by researchers to detect multicollinearity in models such as financial models, fluctuation of market price model, determination of factors responsible for survival of man and market model, etc. The shortfalls of these methods include rigorous computation which discourages researchers from testing for multicollinearity, even when necessary. This paper presents moderate and easy algorithm of the detection of multicollinearity among variables no matter their numbers. Using Min-Max approach with the principle of parallelism of coordinates, we are able to present an algorithm for the detection of multicollinearity with appropriate illustrative examples.
Abstract: Multicollinearity is one of the problems or challenges of modeling or multiple regression usually encountered by Economists and Statisticians. It is a situation where by some of the independent variables in the formulated model are significantly or highly related/correlated. In the past, methods such as Variance Inflation Factor, Eigenvalue and Pro...
Show More
