Augmented mixed beta regression models with skew-normal independent distributions: Bayesian analysis of labor force data

Mohsenkhani, Zohreh Fallah; Mohhamadzadeh, Mohsen; Baghfalaki, Taban

doi:10.1080/03610918.2018.1435802

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Early treatments have been given by Cook, Kieschnick & McCullough (2008) and Ospina & Ferrari (2012). There are also Bayesian treatments by Galvis, Bandyopadhyay & Lachos (2014), Wang & Luo (2016) and Mohsenkhani, Mohhamadzadeh & Baghfalaki (2019). Li (2018) describes how data augmentation may be more computationally efficient for this type of model.…”

Section: Literature Reviewmentioning

confidence: 99%

Regression modelling with the tilted beta distribution: A Bayesian approach

Hahn

2020

Can J Statistics

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Beta regression models are commonly used in the case of a dependent variable y that exists on the range (0,1). However, when y can additionally take on the values of zero and/or one, limitations of the beta distribution and beta regression models become apparent. One recent approach is to use an inflated beta regression model which has discrete point-valued components. In this article, we introduce a new class of regression models for y ∈ [0, 1] that is fully continuous. This allows the entirety of y to be treated as a continuum instead of discontinuously, which appears to be a new development for the literature. We use a Bayesian approach for estimation. We also illustrate the impact of different choices of prior distributions on empirical findings and perform a simulation study examining model fit.

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Section: Literature Reviewmentioning

confidence: 99%

Regression modelling with the tilted beta distribution: A Bayesian approach

Hahn

2020

Can J Statistics

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“…The authors do not present the expected information matrix. Double data inflation for the beta model was considered by Galvis, Bandyopadhyay, and Lachos (2014), Liu and Kong (2015), Mohsenkhani, Mohhamadzadeh, and Baghfalaki (2019), and Nogarotto, Azevedo, and Bazán (2020), but in a Bayesian framework. For similar results using mixture of beta distributions, see Di Brisco and Migliorati (2020).…”

Section: Introductionmentioning

confidence: 99%

Inflated Kumaraswamy regressions with application to water supply and sanitation in Brazil

Bayer

Cribari‐Neto

Santos

2021

Statistica Neerlandica

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Models based on the Kumaraswamy law are used with variables that assume values in (0, 1). In some cases, however, the data contain zeros and/or ones, that is, there is data inflation. We introduce a class of regression models that can be used with such inflated data, namely: the class of inflated Kumaraswamy regression models. We consider inflation at zero, at one, and at both zero and one. We introduce the model and provide closed-form expressions for its score vector and Fisher's information matrix. The proposed model is used to evaluate the impacts of different conditioning variables on the proportion of people who live in households with inadequate water supply and sewage in Brazilian municipalities. Our results reveal that policies directed to increasing the population share with college education in places where it is low are particularly effective in reducing the prevalence of people who live under inadequate sanitation conditions.

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Augmented mixed beta regression models with skew-normal independent distributions: Bayesian analysis of labor force data

Cited by 2 publications

References 34 publications

Regression modelling with the tilted beta distribution: A Bayesian approach

Regression modelling with the tilted beta distribution: A Bayesian approach

Inflated Kumaraswamy regressions with application to water supply and sanitation in Brazil

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