The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates

Mazucheli, Josmar; Menezes, André Felipe Berdusco; Fernandes, Lucas Barbosa; Oliveira, Ricardo Puziol de; Ghitany, M. E.

doi:10.1080/02664763.2019.1657813

Cited by 114 publications

(106 citation statements)

References 40 publications

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“…In a recent paper, Mazucheli et al. (2020) proposed a new parametric approach of quantile regression for limited range response variables. The regression model is based on the UW distribution, which was obtained by Mazucheli et al.…”

Section: Zero‐or‐one Inflated Unit‐weibull Quantile Regression Modelsmentioning

confidence: 99%

“…of the beta distribution does not have an invertible closed form, which hinders its utilization with quantile regression purposes. In contrast to the beta distribution, the unit‐Weibull (UW) distribution (Mazucheli, Menezes, Fernandes, Oliveira, & Ghitany, 2020) has a closed‐form expression for the quantile function. The UW distribution, with support on the unit interval, was proposed by Mazucheli, Menezes, and Ghitany (2018).…”

Section: Introductionmentioning

confidence: 99%

“…For this, Mazucheli et al. (2020) used a simple parameterization of UW distribution that is indexed by quantile and shape parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the above discussion, the main aim of this paper is to propose a parametric quantile regression model that is tailored for situations where the response variable is measured continuously on the intervals [0, 1) or (0, 1] based on reparameterized UW distribution (Mazucheli et al., 2020). In particular, the proposed model assumes that the response variable has a mixed continuous‐discrete distribution with probability mass at zero or one.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the proposed model assumes that the response variable has a mixed continuous‐discrete distribution with probability mass at zero or one. The reparameterized UW distribution (Mazucheli et al., 2020) 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 quantile regression quantifies the association of the explanatory variables with a quantile of a dependent variable where the response variable is measured continuously on the intervals [0, 1) or (0, 1].…”

Section: Introductionmentioning

confidence: 99%

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A parametric quantile regression approach for modelling zero‐or‐one inflated double bounded data

2021

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Over the last decades, the challenges in applied regression have been changing considerably, and full probabilistic modeling rather than predicting just means is crucial in many applications. Motivated by two applications where the response variable is observed on the unit‐interval and inflated at zero or one, we propose a parametric quantile regression considering the unit‐Weibull distribution. In particular, we are interested in quantifying the influence of covariates on the quantiles of the response variable. The maximum likelihood method is used for parameters estimation. Monte Carlo simulations reveal that the maximum likelihood estimators are nearly unbiased and consistent. Also, we define a residual analysis to assess the goodness of fit.

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Section: Zero‐or‐one Inflated Unit‐weibull Quantile Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For this, Mazucheli et al. (2020) used a simple parameterization of UW distribution that is indexed by quantile and shape parameters.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A parametric quantile regression approach for modelling zero‐or‐one inflated double bounded data

2021

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Study of a bounded interval perks distribution with quantile regression analysis

Al‐Essa,

Shafiq,

Ozonur

et al. 2024

Statistical Analysis

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In this article, a novel bounded interval model called the unit‐Perks model is developed by suitably transforming the positive random variable of the Perks distribution. Numerous statistical features of the bounded interval Perks model are being explored based on the expansion of the density function. Eight distinct estimation approaches are being used to estimate the parameters of the unit‐Perks model. A throughout simulation analysis is also included to evaluate the precision of the resulting estimators from eight estimating approaches. Two real bounded interval data sets are being utilized to investigate the practical applicability of the unit‐Perks model. A comparison is also made to determine which method of estimation works better for the given model. According to a comparison of eight different estimation approaches, the maximum likelihood estimation approach outperformed than the other seven estimating approaches. The unit‐perks model is then used to introduce the quantile regression model named as quantile unit‐Perks distribution. Application to real data set for the quantile unit‐Perks distribution is also performed. The quantile residuals are used for the residual analysis of the fitted regression model. On the basis of mathematical, computational, and pictorial evidences, it is concluded that the presented model exhibited greater modeling capabilities.

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A new quantile regression model with application to human development index

Cordeiro,

Rodrigues,

Prataviera

et al. 2023

Comput Stat

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The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates

Cited by 114 publications

References 40 publications

A parametric quantile regression approach for modelling zero‐or‐one inflated double bounded data

A parametric quantile regression approach for modelling zero‐or‐one inflated double bounded data

Study of a bounded interval perks distribution with quantile regression analysis

A new quantile regression model with application to human development index

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