International Journal of Data Science and Analysis
Volume 6, Issue 6, December 2020, Pages: 163-169
Received: Oct. 6, 2020;
Accepted: Oct. 21, 2020;
Published: Oct. 30, 2020
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Julius Sang, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Antony Ngunyi, Department of Statistics and Actuarial Science, Dedan Kimathi University of Technology, Nyeri, Kenya
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.
Post-Harvest Loss Modeling of Maize Produce in Kenya, International Journal of Data Science and Analysis.
Vol. 6, No. 6,
2020, pp. 163-169.
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