Research Article
Negative Binomial Three Parameter Lindley Distribution and Its Properties
Wanjala Anjela*,
George Muhua
Issue:
Volume 10, Issue 1, February 2024
Pages:
1-5
Received:
17 October 2023
Accepted:
2 January 2024
Published:
14 June 2024
Abstract: Many researchers have proposed mixed distributions as one of the most important methods for obtaining new probability distributions. Several studies have shown that mixed Negative Binomial distributions fits count data better than Poisson and Negative Binomial distribution itself. In this paper, we introduce a mixed distribution by mixing the distributions of negative binomial and three Parameter Lindley distribution. This new distribution has a thick tail and may be considered as an alternative for fitting count data with over dispersion. The parameters of the new distribution are estimated using MLE method and properties studied. Special cases of the new distribution and also identified. A simulation study carried out shows that the ML estimators give the parameter estimates close to the parameter when the sample is large, that is, the bias and variance of the parameter estimates decrease with increase in sample size showing the consistent nature of the new compound distribution. The study also compares the performance of the new distribution over distributions of Poisson, Negative Binomial, Negative Binomial oneParameter Lindley Distribution, Negative Binomial two Parameter distribution, three parameter Lindley distribution using a real count over dispersed dataset and the results shows that Negative Binomial three parameter Lindley distribution gave the smallest Kolmogorov Smirnov test statistic, AIC and BIC as compared to other distributions, hence the new distribution provided a better fit compared to other distributions under study for fitting over dispersed count data.
Abstract: Many researchers have proposed mixed distributions as one of the most important methods for obtaining new probability distributions. Several studies have shown that mixed Negative Binomial distributions fits count data better than Poisson and Negative Binomial distribution itself. In this paper, we introduce a mixed distribution by mixing the distr...
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Research Article
Adoption of Coffee Technologies: A Multivariate Probit Model
Megdelawit Temesgen*,
Sisay Debeb
Issue:
Volume 10, Issue 1, February 2024
Pages:
6-13
Received:
22 November 2023
Accepted:
11 January 2024
Published:
15 August 2024
Abstract: The sector of agriculture in Ethiopia is a source of livelihood for over 80% population residing in rural areas. It contributes about 50% to the national value of production. The country has huge potential to increase coffee production as it endowed with suitable elevation, temperature, and soil fertility, indigenous quality plantation materials, and sufficient rainfall in coffee growing belts of the country. Adoption of improved coffee varieties and recommended coffee management practice together have a significant effect on annual coffee production. The study was aimed to see the adoption rate and intensity of coffee technologies which are improved coffee Varity and coffee management practice, determinant of adoption of coffee technologies in Jima zone south western Ethiopia. A total of 196 sampled households from three woreda in the zone and 430 plots of 196 farmers household is considered in the survey. The study develops a multivariate probit model econometric model of farmers' choice of combination of coffee technologies. And two primary results were found. First, adoption rate and intensity of Improved coffee variety is greater than adoption of coffee management practice. Secondly adoption of coffee technologies determined by many institutional, resource and other related factor. This implies that policy makers and other stakeholders promoting a combination of technologies can enhance coffee yield through reducing production costs and decreasing coffee vulnerability to disease.
Abstract: The sector of agriculture in Ethiopia is a source of livelihood for over 80% population residing in rural areas. It contributes about 50% to the national value of production. The country has huge potential to increase coffee production as it endowed with suitable elevation, temperature, and soil fertility, indigenous quality plantation materials, a...
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