Beta Regression for the Indicator Values of Well-Being Index For Provinces in Turkey

Aktaş, Serpil; Ünlü, Hande Konşuk

doi:10.30931/jetas.321165

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…Wheat commercialization index score which ranges from 0.1 to 0.95 is the outcome variable for this study. Although the beta regression model has been employed in a different discipline to address these problems [48,49,[53][54][55], it has rarely been used in the context of crop commercialization index scores. Consequently, this study applied the beta regression model to analyze the relationship between the wheat commercialization index score and exogenous variables.…”

Section: Econometric Modelmentioning

confidence: 99%

Determinants of Wheat Commercialization among Smallholder Farmers in Debre Elias Woreda, Ethiopia

Endalew

Aynalem

Assefa³

et al. 2020

Advances in Agriculture

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Even though wheat commercialization has got great emphasis, smallholder farmers face challenges to participate in the wheat market. As a result, understanding the intensity of smallholder farmers’ wheat commercialization and factors affecting wheat commercialization has vital importance to tackle the problem. Consequently, this study was undertaken in northwestern Ethiopia to measure smallholder farmers’ wheat commercialization and analyze factors that hamper wheat commercialization in the study area. For this purpose, a systematic sampling technique has been employed to select a total of 154 smallholder wheat producer household heads. A semistructured questionnaire has been used as a principal primary data collection method. Similarly, the output commercialization index and the beta regression model have been employed for data analysis. The result of the study showed that 23.4%, 51.9%, and 24.7% of smallholder farmers were subsistence, semicommercialized, and commercialized, respectively. The result indicated that the majority of smallholder farmers are semicommercial wheat producers. Moreover, the beta regression result indicated that educational status, number of oxen, land size allocated to wheat production, farming experience in wheat production, extension service, and market distance are major factors for smallholder farmer’s wheat commercialization. Therefore, an attempt to increase smallholder farmers’ wheat commercialization should give special priority for significant explanatory variables.

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Section: Econometric Modelmentioning

confidence: 99%

Determinants of Wheat Commercialization among Smallholder Farmers in Debre Elias Woreda, Ethiopia

Endalew

Aynalem

Assefa³

et al. 2020

Advances in Agriculture

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“…This was as with Unlu & Aktas (2017) who used the parametrized beta regression in the modeling of well-being index data of Turkey [1]. It was also an extension of the Rojas (2019), Hugo (2016), Ayieko et al (2013) and Vasanthi et al (2015) who modeled the maize produce and the factors affecting its produce [2,6,14,4].…”

Section: Discussionmentioning

confidence: 80%

“…Unlu & Aktas (2017) applied the parametrized beta regression in modeling the well-being index data of Turkey [1]. Rojas (2019) had a research on operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya using the multiple linear regression model [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Post-Harvest Loss Modeling of Maize Produce in Kenya

Sang¹,

Wanjoya²,

Ngunyi³

2020

IJDSA

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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.

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“…The implementation of the proposed estimator is illustrated by a study applied to the well-being index of Turkey in 2015 [32]. The index involves the aspects of accommodation, jobs, income and wealth, health, education, climate, protection, public engagement and access to community resources and social life.…”

Section: Real Data Applicationmentioning

confidence: 99%

Dawoud–Kibria Estimator for Beta Regression Model: Simulation and Application

Abonazel

Dawoud

Awwad

et al. 2022

Front. Appl. Math. Stat.

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The linear regression model becomes unsuitable when the response variable is expressed as percentages, proportions, and rates. The beta regression (BR) model is more appropriate for the variable of this form. The BR model uses the conventional maximum likelihood estimator (BML), and this estimator may not be efficient when the regressors are linearly dependent. The beta ridge estimator was suggested as an alternative to BML in the literature. In this study, we developed the Dawoud–Kibria estimator to handle multicollinearity in the BR model. The properties of the new estimator are derived. We compared the performance of the estimator with the existing estimators theoretically using the mean squared error criterion. A Monte Carlo simulation and a real-life application were carried out to show the benefits of the proposed estimator. The theoretical comparison, simulation, and real-life application results revealed the superiority of the proposed estimator.

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Beta Regression for the Indicator Values of Well-Being Index For Provinces in Turkey

Cited by 14 publications

References 8 publications

Determinants of Wheat Commercialization among Smallholder Farmers in Debre Elias Woreda, Ethiopia

Determinants of Wheat Commercialization among Smallholder Farmers in Debre Elias Woreda, Ethiopia

Post-Harvest Loss Modeling of Maize Produce in Kenya

Dawoud–Kibria Estimator for Beta Regression Model: Simulation and Application

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