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Optimal Allocation in Domains Mean Estimation Using Double Sampling with Non-Linear Cost Function in the Presence of Non-Response
Alilah David Anekeya,
Ouma Christopher Onyango,
Nyongesa Kennedy
Issue:
Volume 7, Issue 2, March 2018
Pages:
45-57
Received:
13 December 2017
Accepted:
5 January 2018
Published:
12 February 2018
Abstract: Studies have been carried out on domain mean estimation using non-linear cost function. However little has been done on domain stratum estimation using non-linear cost function using ratio estimation in the presence of non-response. This study develops a method of optimal stratum sample size allocation in domain mean estimation using double sampling with non-linear cost function in the presence of non- response. To obtain an optimum sample size, Lagrangian multiplier technique is employed by minimizing precision at a specified cost. In the estimation of the domain mean, auxiliary variable information in which the study and auxiliary variables both suffers from non-response in the second phase sampling is used. The expressions of the biases and mean square errors of proposed estimator has also been obtained.
Abstract: Studies have been carried out on domain mean estimation using non-linear cost function. However little has been done on domain stratum estimation using non-linear cost function using ratio estimation in the presence of non-response. This study develops a method of optimal stratum sample size allocation in domain mean estimation using double samplin...
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Modeling Self Medication Risk Factors (A Case Study of Kiambu County, Kenya)
Thomas Mageto,
Allan Zablon
Issue:
Volume 7, Issue 2, March 2018
Pages:
58-66
Received:
15 January 2018
Accepted:
29 January 2018
Published:
27 February 2018
Abstract: In this paper self-medication risk factors are investigated and multivariate model proposed. A random sample of four major hospitals was selected, one from each sub-county and sample of 728 patients selected from selected hospitals using stratified random sampling. The data was collected using semi structured questionnaires and analyzed in R program after cleaning for non-response. Preliminary analysis was carried to check for statistical significance of the risk factors of age, gender, income, marital, education, employment and insurance status. All proposed risk factors were statistically significant except employment factor when using chi-square test for each of discrete variables while both age and income continuous variables were significant at α =0.05 level of significance when fitting simple logistic regression model. The initial multivariate logistic regression model was fitted and variables of marital and insurance status of persons were statistically insignificant and therefore improved model was fitted less marital and insurance factors. The overall significance of the model was determined using Hosmer and Lemeshow goodness-of-fit test and the model recorded p-value of 0.7751 that indicates that there is no significant difference between observed and predicted probability, therefore the model would be used to predict chance of self-medication in the presence of significant risk factors. In conclusion therefore there is need to initiate legislation on policies that will guide self-medication that include provision of necessary knowledge and regulating the practice to avoid over dose, wrong prescriptions and emergence of human pathogen resistance microorganisms or serious consequences like resistance to medication in future guided by the prevalence results obtained from proposed model.
Abstract: In this paper self-medication risk factors are investigated and multivariate model proposed. A random sample of four major hospitals was selected, one from each sub-county and sample of 728 patients selected from selected hospitals using stratified random sampling. The data was collected using semi structured questionnaires and analyzed in R progra...
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Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community
Abraham Kipkosgei Lagat,
Anthony Gichuhi Waititu,
Anthony Kibera Wanjoya
Issue:
Volume 7, Issue 2, March 2018
Pages:
67-79
Received:
1 September 2017
Accepted:
18 September 2017
Published:
16 March 2018
Abstract: There has been increased interest of late on the application of nonlinear methods to economic and financial data due to their robustness in handling large and complex data. With increasingly complex ‘big data’, focus has shifted into use of robust techniques in analysis of data. Various nonlinear approaches have so far been established including support vector machine which is widely adapted in classification and regression problems. This research project applied support vector regression technique and neural network models in modeling and forecasting economic growth for the five countries in the East Africa Community including Kenya, Uganda, United Republic of Tanzania, Rwanda and Burundi. Data for the period 1990 to 2014 from World Bank databases was used for the research. Support vector model and neural network models were trained using the data for the 1990-2002 whereas the remaining data was used for prediction performance to determine the robustness of the two models on external datasets. The study revealed that specific country models had better performance compared to the combined model and that although the two models compared similarly under specific-country models, the neural network performed better in most countries. The study recommends the use of the two machine learning techniques in economic growth modeling. It also recommends that the performance be compared with the traditional econometric models but using countries with more data periods.
Abstract: There has been increased interest of late on the application of nonlinear methods to economic and financial data due to their robustness in handling large and complex data. With increasingly complex ‘big data’, focus has shifted into use of robust techniques in analysis of data. Various nonlinear approaches have so far been established including su...
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Pricing of a European Call Option Under a Local Volatility Interbank Offered Rate Model
Winnie Mbusiro Chacha,
Caroline Njenga,
Wilson Mahera
Issue:
Volume 7, Issue 2, March 2018
Pages:
80-84
Received:
1 February 2018
Accepted:
24 February 2018
Published:
22 March 2018
Abstract: This research seeks to give insight on how advances in developed money markets can be reflected towards the establishment of derivatives markets in under developed and developing financial markets. The dynamics of the London interbank offered rate, for the developed financial market and the Kenyan interbank offered rate, for the developing financial markets, are compared. For the period between 2013-2015, both interest rates are found to have the same underlying dynamics. A European caplet is priced using the local volatility interbank offered rate model. The local volatility model is used as it captures the volatility smiles more efficiently in one sweep. Thereafter, the local volatility interbank offered rate model is formulated and used to price the European caplet for the developing markets.
Abstract: This research seeks to give insight on how advances in developed money markets can be reflected towards the establishment of derivatives markets in under developed and developing financial markets. The dynamics of the London interbank offered rate, for the developed financial market and the Kenyan interbank offered rate, for the developing financia...
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Employee’s Job Satisfaction and Supervisors Performance at Debre Berhan University
Issue:
Volume 7, Issue 2, March 2018
Pages:
85-91
Received:
5 March 2018
Accepted:
14 March 2018
Published:
22 March 2018
Abstract: In order to ensure the achievement of organization’s goals, it should create an atmosphere of commitment and cooperation for its employees through policies that facilitate employee satisfaction. The main objective of the study is identifying factors affecting employee’s job satisfaction and supervisor performance at Debre Berhan University. In order to meet the objective of the study a sample of 272 employees have taken. And consecutive statistical methods have been used. Both descriptive and inferential group of statistical tools have been applied. The descriptive result of the study reveals that about 63.2% of the sampled employees have unsatisfied and the rest 36.8% of them have satisfied with the working condition of the university. From logistic regression analysis of job satisfaction it is evident that the way of promotion, encouragement of research work, accessibility of office, merit based job allocation, recognition for contributions, responding the need of home and facility of working materials have significant effect on employees job satisfaction at 5% level of significant. Multiple linear regression also shows that handles employees complaints, does not allow special privilege, availability of supervisor for staffs, being respectful, good leading skill, trust on clerk, and sense of responsibility have significant effect on the supervisor performance at 5% level of significance. Finally chi-square test of association of overall job satisfaction and overall supervisor performance shows that there is a strong association between job satisfaction and performance of immediate supervisors. from consecutive result of the study it is recommended that; the management of the university should improve the way of promotion for staffs based on the legislation of the university, there should be motivated encouragement and recognition for researchers so that scientific environment can be created in the compound, the university should also work hard to satisfy the need of staffs for home and the allocation of supervisors in different hierarchy should be merit based.
Abstract: In order to ensure the achievement of organization’s goals, it should create an atmosphere of commitment and cooperation for its employees through policies that facilitate employee satisfaction. The main objective of the study is identifying factors affecting employee’s job satisfaction and supervisor performance at Debre Berhan University. In orde...
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