Bayesian beta regression for bounded responses with unknown supports

Zhou, Huaiying; Huang, Xing

doi:10.1016/j.csda.2021.107345

Cited by 13 publications

(10 citation statements)

References 66 publications

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“…In multiple linear regressions, Cox and Snell R Square coefficients are interpreted the same as the coefficient of determination (R 2 ), but since the maximum value can sometimes be less than 1, it is hard to interpret, so it is rarely used. To ensure the value varies Prevalence of poor sleep quality from 0 to 1, Cox and Snell squares are modified to create Nagelkerke R squares for this model of 0.074 (Zhou and Huang, 2022). The value 0.07 means that the independent variable explains 7.4% of the variability of the dependent variable, and the rest is explained by other variables, not in the model.…”

Section: Resultsmentioning

confidence: 99%

An assessment of prevalence of poor sleep quality among construction workers in Southern India

Sathvik

Krishnaraj²,

Awuzie³

2022

BEPAM

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PurposeSleep quality has been associated with health and safety issues influencing construction labour productivity in developing countries. Despite its significantly adverse contribution to these facets if left unattended to, limited studies have sought to establish its prevalence and causal factors in labour-intensive contexts. This study aims to bridge the gap between the prevalence and casual factors of poor sleep quality among construction workers.Design/methodology/approachAdopting a case study research design, data were collected from a randomly selected sample of construction workers (n = 475) recruited from four construction-based corporations in Southern India. Self-administered questionnaires comprising a Pittsburgh Sleep Quality Index (PSQI) scale was used to measure sleep quality among respondents alongside demographic characteristics, lifestyle preferences and work-habits data. Data were analysed using multivariate logistic regression analysis and descriptive statistical techniques.FindingsSlightly more than a third of the construction workers (n = 33.9%) surveyed experienced poor sleep quality based on a PSQI method score of = 5. Also, the nexus between demographic characteristics, lifestyle preferences and work habits on the incidence of poor sleep quality among construction workers was confirmed.Practical implicationsThe study contributes to the contemporary discourse on improving sleep health of construction workers to enhance their well-being thereby enabling their contribution towards achieving improved construction labour productivity.Originality/valueThis study makes an original contribution to the extent that it seeks to not only determine the prevalence of sleep quality within the construction industry and associated causal factors but to explore its implications on construction labour productivity.

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Section: Resultsmentioning

confidence: 99%

An assessment of prevalence of poor sleep quality among construction workers in Southern India

Sathvik

Krishnaraj²,

Awuzie³

2022

BEPAM

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“…Existing semiparametric methods for modal regression only introduce parametric ingredients in the regression function, i.e., the conditional mode of the response, with the mode-zero error distribution left in a nonparametric form [18,[51][52][53][54][55][56][57]. The few recently proposed parametric modal regression models all impose stringent parametric assumptions on the error distribution [19][20][21]. Our proposed flexible Gumbel distribution greatly alleviates concerns contributing to data scientists' reluctance to adopt a parametric framework when drawing inferences for the mode.…”

Section: Discussionmentioning

confidence: 99%

“…For each component distribution of FG, the mean and median are both some simple shift of the mode, with each shift solely determined by the scale parameter. Because the two components in (3) share a common mode θ, the mode of Y is also θ, and thus the FG distribution is convenient to use when one aims to infer the mode as a central tendency measure, or to formulate parametric modal regression models [19][20][21]. One can easily show that the mean of…”

Section: The Flexible Gumbel Distributionmentioning

confidence: 99%

The Flexible Gumbel Distribution: A New Model for Inference about the Mode

Liu,

Huang,

Zhou

2024

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A new unimodal distribution family indexed via the mode and three other parameters is derived from a mixture of a Gumbel distribution for the maximum and a Gumbel distribution for the minimum. Properties of the proposed distribution are explored, including model identifiability and flexibility in capturing heavy-tailed data that exhibit different directions of skewness over a wide range. Both frequentist and Bayesian methods are developed to infer parameters in the new distribution. Simulation studies are conducted to demonstrate satisfactory performance of both methods. By fitting the proposed model to simulated data and data from an application in hydrology, it is shown that the proposed flexible distribution is especially suitable for data containing extreme values in either direction, with the mode being a location parameter of interest. Using the proposed unimodal distribution, one can easily formulate a regression model concerning the mode of a response given covariates. We apply this model to data from an application in criminology to reveal interesting data features that are obscured by outliers.

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“…In all three aforementioned works, frequentist likelihood‐based methods are developed to infer model parameters. Most recently, Zhou and Huang (2022) unified the mean regression and modal regression in a Bayesian framework by reparameterizing a four‐parameter beta distribution with an unknown support so that the mean or the mode of Y depends on

\bm {X}

. Earlier works on Bayesian modal regression, including parametric and nonparametric methods, can also be found in Aristodemou (2014, Chapter 2).…”

Section: Introductionmentioning

confidence: 99%

Parametric modal regression with error in covariates

Liu,

Huang

2024

Biometrical J

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An inference procedure is proposed to provide consistent estimators of parameters in a modal regression model with a covariate prone to measurement error. A score‐based diagnostic tool exploiting parametric bootstrap is developed to assess adequacy of parametric assumptions imposed on the regression model. The proposed estimation method and diagnostic tool are applied to synthetic data generated from simulation experiments and data from real‐world applications to demonstrate their implementation and performance. These empirical examples illustrate the importance of adequately accounting for measurement error in the error‐prone covariate when inferring the association between a response and covariates based on a modal regression model that is especially suitable for skewed and heavy‐tailed response data.

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Bayesian beta regression for bounded responses with unknown supports

Cited by 13 publications

References 66 publications

An assessment of prevalence of poor sleep quality among construction workers in Southern India

An assessment of prevalence of poor sleep quality among construction workers in Southern India

The Flexible Gumbel Distribution: A New Model for Inference about the Mode

Parametric modal regression with error in covariates

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