Beta Regression Models: Joint Mean and Variance Modeling

Cepeda-Cuervo, Edilberto

doi:10.1080/15598608.2014.890983

Cited by 21 publications

(14 citation statements)

References 6 publications

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“…Due to the complexity of the performed models (univariate, multiple and for repeated measures beta regression models), Bayesian inference, based on Markov Chain Monte Carlo (MCMC) method, was adopted as model estimation procedure [33]). The goodness of fit of the models was evaluated by pseudo-R 2 (ranges 0-1) computed as the square of the correlation between the fitted values (drawn from the posterior predictive distribution) and the observed values [34].…”

Section: Discussionmentioning

confidence: 99%

The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: A European Alzheimer's Disease Consortium study

Bosco

Redolfi

Bocchetta

et al. 2017

Alzheimer's & Dementia

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Section: Discussionmentioning

confidence: 99%

The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: A European Alzheimer's Disease Consortium study

Bosco

Redolfi

Bocchetta

et al. 2017

Alzheimer's & Dementia

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“…As a consequence, using the maximum likelihood of the beta regression model, the use-intensification levels of SAT of the household farmers in the area were isolated. A modification of the beta density parameterization in terms of the variate mean and a precision parameter underpins the beta regression model [21]. In its most general form, the probability beta density y-B (p, q) for the dependent variable y is defined as; ϝ(y; p, q) = ( )…”

Section: Methodsmentioning

confidence: 99%

Econometric Analysis of Agricultural Intensification Techniques of Household Farmers in Nigeria

Osuji¹,

Onyeneke²,

Balogun³

et al. 2021

ujar

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Agriculture is the principal source of income for the majority of Nigerian rural households, but it has suffered significant setbacks in recent years, resulting in lower productivity and returns on investment. The study looked at the econometric analysis of household farmers' agricultural intensification strategies in Imo State, Nigeria. With the help of standardized questionnaires, 198 household farmers were selected using a multi-stage sample technique. The Beta regression model and descriptive statistics were used to analyze the data collected. Cassava, maize, and pumpkin leaves had the highest average outputs of 88.712kg, 87.901kg, and 85.990kg, respectively, which dominated the entire production cycle. Planting materials, soil nutrients, and land improvement activities cost the most, at N81.637. 75, N74, 402.82, and N71, 906.80, respectively. At 1% and 5% levels, the coefficients of age, sex, education, net farm income, farm size, extension contacts, and farming experience were statistically significant and influenced the intensification of sustainable agricultural techniques (SAT) in the area. The study recommends farmers to embrace effective and long-term agricultural technologies in order to boost farm production and output. Extension personnel should also meet with household farmers on a regular basis to communicate important information about new technologies.

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“…It is commonly used in fields such as education, finance, and social sciences. (Cepeda-Cuervo, 2015).…”

Section: Beta Regression Modelmentioning

confidence: 99%

Estimation of High School Entrance Examination Success Rates Using Machine Learning and Beta Regression Models

Koç¹,

Akin²

2022

Journal of Intelligent Systems: Theory and Applications

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Education is the foundation of economic, social, and cultural development for every individual and society as a whole. Students are accepted to secondary education institutions with the high school entrance examination made by the Ministry of National Education in Turkey. In this study, the success rates of the students who took the high school entrance examination in Turkey's 81 provinces in 2019 were handled with the machine learning regression and beta regression model. The present paper aimed to model, predict, and explain students' success rates using variables such as divorce rate, gross domestic product, illiteracy, and higher education populations. Support vector regression, random forest, decision tree, and beta regression model were applied to estimate success rates. Two models with the highest R 2 value were found to be beta regression and random forest models. When the prediction errors of beta regression and random forest model were examined, it seemed to be that the random forest model is relatively superior to the beta regression model in predicting the success rates. While the beta regression model was the best predictor of the success rates of Çanakkale province, the random forest model predicted the success rates of Ankara well. Also, it was seen that the variables found to be significant in the beta regression model for success rates were also crucial in the random forest model. It is recommended to use both the beta and random forest models to estimate the students' success rates.

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Beta Regression Models: Joint Mean and Variance Modeling

Cited by 21 publications

References 6 publications

The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: A European Alzheimer's Disease Consortium study

The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: A European Alzheimer's Disease Consortium study

Econometric Analysis of Agricultural Intensification Techniques of Household Farmers in Nigeria

Estimation of High School Entrance Examination Success Rates Using Machine Learning and Beta Regression Models

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