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Post-Harvest Loss Modeling of Maize Produce in Kenya
Julius Sang,
Anthony Wanjoya,
Antony Ngunyi
Issue:
Volume 6, Issue 6, December 2020
Pages:
163-169
Received:
6 October 2020
Accepted:
21 October 2020
Published:
30 October 2020
Abstract: The classical linear model is commonly used to model the relationship between a response variable and a set of explanatory variables. The normality assumption is usually required so as to ease the hypothesis testing for the various linear regression models but it can be misleading for a proportional response variable that is bounded. This makes the ordinary least squares regression inappropriate for a regression model with a bounded dependent variable. This research proposes the fractional beta regression model as an alternative to help examine the determinants of post-harvest loss management of maize produce for farmers in Kenya. The response variable (Post-Harvest Loss Coefficient (PHLC)) is assumed to have a mixed continuous-discrete distribution with probability mass between zero and one. The fractional beta distribution is used to describe the continuous component of the model, since its density has a wide range of different shapes depending on the values of the two parameters that index the distribution. The study uses a suitable parameterization of the beta law in terms of its mean and a precision parameter, the parameters of the mixture distribution shall be modeled as functions of regression parameters. The considered parameters are Agriculture, Storage, Education, Fumigation and Transport. Inference on parameters, model diagnostics and model selection tools for the fractional beta regression is also be provided. Data used for this research was purely primary data which was collected from Uasin Gishu County, Kenya maize farmers through administration of a research questionnaire.
Abstract: The classical linear model is commonly used to model the relationship between a response variable and a set of explanatory variables. The normality assumption is usually required so as to ease the hypothesis testing for the various linear regression models but it can be misleading for a proportional response variable that is bounded. This makes the...
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Bayesian Analysis of Zero-Truncated Poisson Model: Application to the Self-Controlled Case-series Design
Henry Athiany,
Anthony Wanjoya,
George Orwa,
Samuel Mwalili
Issue:
Volume 6, Issue 6, December 2020
Pages:
170-182
Received:
12 October 2020
Accepted:
28 October 2020
Published:
4 November 2020
Abstract: A Bayesian Self-Controlled Case-Series (BSCCS) method is proposed and used to estimate the relative risk of an adverse drug event (ADE) given transient exposure to a drug or vaccine. Markov Chain Monte Carlo (MCMC) methods through WinBUGS are used to estimate parameters of the model given different settings and sample sizes. The method explores full posterior distribution for the model to obtain the relative risk estimates which at times is a challenge in likelihood analysis of complex models. Data was simulated for 10, 20 or 50 children aged between 365 and 730 days, and received their first dose of the measles, mumps, and rubella (MMR) vaccine within this follow-up period. Each child had the outcome event – viral-meningitis, in the follow-up period. Results of the data analysis indicated an increased risk of viral meningitis within 14-35 days post vaccination. Results of Bayesian approach are quite similar to the MLE risk estimates, assuming a non-informative prior. However, with more informative priors, BSCCS method produced better results with narrow credible intervals. For the real data, children aged 365 and 730 days, exposed to MMR vaccine, with viral meningitis (single exposure) were considered. While the frequentist approach estimated the incidence rate ratio (IRR) as IRR 12.037 (95% CI (3.002 - 48.259)), the Bayesian estimate was IRR 8.971 (95% CI 2.869 - 27.994). This is similar to the MLE results but with narrow credible intervals. In all cases, there is significantly higher risk of viral meningitis within 14-35 days post MMR vaccination. Results from the simulation study and real data revealed that the BSCCS model fitted better than the SCCS model.
Abstract: A Bayesian Self-Controlled Case-Series (BSCCS) method is proposed and used to estimate the relative risk of an adverse drug event (ADE) given transient exposure to a drug or vaccine. Markov Chain Monte Carlo (MCMC) methods through WinBUGS are used to estimate parameters of the model given different settings and sample sizes. The method explores ful...
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Developing and Implementing Big Data Analytics in Marketing
Issue:
Volume 6, Issue 6, December 2020
Pages:
183-203
Received:
30 October 2020
Accepted:
11 November 2020
Published:
19 November 2020
Abstract: Big Data represents the greatest game-changing chance and change in outlook for marketing since the creation of the telephone or the Web going standard. Big Data alludes to the ever-expanding volume, velocity, variety, variability and multifaceted nature of data. Big Data is the key result of the new promoting scene, conceived from the computerized world we currently live in for marketing associations. The expression "big data" doesn't simply allude to the information itself; it additionally alludes to the difficulties, capacities and skills related with putting away and examining such gigantic data sets to help a degree of decision-making that is more precise and timely than anything recently endeavored. Because of the many benefits of big data, the big data applications have appeared, and they can play important roles especially in making companies take informative business decisions in different fields, such as, healthcare, banking, manufacturing, media and entertainment, education and transportation and many others. This paper concentrates on the importance of Big Data Analytics nowadays, especially in the marketing process inside companies, as well as challenges and obstacles facing Big Data analytics, and a case study of a bank wanting to market a new financial tool to its customers is studied using R tool.
Abstract: Big Data represents the greatest game-changing chance and change in outlook for marketing since the creation of the telephone or the Web going standard. Big Data alludes to the ever-expanding volume, velocity, variety, variability and multifaceted nature of data. Big Data is the key result of the new promoting scene, conceived from the computerized...
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Non-linear Approximations of Shape and Location Parameters in the Poisson Inverse Gaussian Model in Analysis of Infectious Count Data
Symon Kamuyu Matonyo,
Oscar Ngesa,
Anthony Wanjoya
Issue:
Volume 6, Issue 6, December 2020
Pages:
204-212
Received:
13 November 2020
Accepted:
21 November 2020
Published:
30 November 2020
Abstract: Statistical models create a basis for analysis of infectious disesase count. These data sets exhibit unique characteristics such as low counts, delayed reporting, underreporting amoung others. The tendency to model these counts using linear models with their simplicity is common with most research. Further, the assumption of a fixed dispersion in modeling infectious disease counts is quite high. Prediction relating to infectious disease counts have been based on the Poisson model framework. The extension of the Poisson models such NB and PIG distributions have gained popularity over the recent past in modeling count responses showing over dispersion relative to the Poisson distribution. In this study we propose non-linear models for these data sets, modeling the mean and dispersion parameters as additive terms. Negative Binomial (NB) and Poisson Inverse Gaussian (PIG) glm models with a fixed and a varying dispersion parameter and compare them with NB GAM and PIG GAM with both mean and dispersion modeled as additive terms. The model are fitted to over dispersed infectious counts, Salmonella Hadar data set. Residual plots are constructed to explore the quality of fits and analysis goodness of fit is carried out to access the best fitting model. The study results reveal better performance of the PIG models on both the linear and non linear model platforms. Further, modelling both the mean and dispersion proved better as compared to models assuming the dispersion as a constant.
Abstract: Statistical models create a basis for analysis of infectious disesase count. These data sets exhibit unique characteristics such as low counts, delayed reporting, underreporting amoung others. The tendency to model these counts using linear models with their simplicity is common with most research. Further, the assumption of a fixed dispersion in m...
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Inter-Arrival Time Modeling of Threshold Scores in Mathematics Among School Pupils; A Case of Acacia Crest School, Kenya
Stella Mutahi,
Thomas Mageto,
Antony Ngunyi
Issue:
Volume 6, Issue 6, December 2020
Pages:
213-219
Received:
3 December 2020
Accepted:
10 December 2020
Published:
22 December 2020
Abstract: Mathematical literacy is the ability to use numbers to help solve real-world problems. It focuses on pupils' ability to analyze, justify and communicate ideas effectively with regard to formulating, solving and interpreting Mathematical problems in a variety of forms and situations. The study modeled above threshold scores in mathematics among school pupils as an indicator for being mathematically literate. Modeling was on the inter-arrival times for pupils scoring above threshold scores (Mathematics mean score) for a given sample of pupils in their mid and end of term examinations. The Poisson distribution has been widely used as a statistical procedure for modeling inter-arrival times for count data outcomes. However, for heavy-tailed inter-arrival times of successive outcomes, the Poisson distribution exhibits an empirical observational failure thus setting up a framework for the use of other distributions that can handle such heavy-tailed data. The study used the generalized Gumbel and Weibull inter-arrival time distributions which were assumed to nest the standard Poisson distribution in which Weibull inter-arrival gave a better fit to the data. Data used was secondary data on pupil performance in Mathematics in relation to other subjects from Acacia Crest School.
Abstract: Mathematical literacy is the ability to use numbers to help solve real-world problems. It focuses on pupils' ability to analyze, justify and communicate ideas effectively with regard to formulating, solving and interpreting Mathematical problems in a variety of forms and situations. The study modeled above threshold scores in mathematics among scho...
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