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Fuzzy Semi-Parametric Logistic Quantile Regression Model
Wasit Journal for Pure Sciences, 2023 In this paper, the fuzzy semi-parametric logistic quantile regression model was studied in the absence of special conditions in the classical regression models.
Ahmed Razzaq, Ayad H. shemaila
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Pyramid Quantile Regression [PDF]
Journal of Computational and Graphical Statistics, 2019 Quantile regression models provide a wide picture of the conditional distributions of the response variable by capturing the effect of the covariates at different quantile levels. In most applications, the parametric form of those conditional distributions is unknown and varies across the covariate space, so fitting the given quantile levels ...
T. Rodrigues +2 more
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Simulation Study of Autocorrelated Error Using Bayesian Quantile Regression
Science and Technology Indonesia, 2020 The purpose of this study is to compare the ability of the Classical Quantile Regression method and the Bayesian Quantile Regression method in estimating models that contain autocorrelated error problems using simulation studies.
Nayla Desviona, Ferra Yanuar
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Quantile regression in high-dimension with breaking [PDF]
Journal of Statistical Theory and Applications (JSTA), 2013 The paper considers a linear regression model in high-dimension for which the predictive variables can change the influence on the response variable at unknown times (called change-points).
Gabriela Ciuperca
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Cauchy: Jurnal Matematika Murni dan Aplikasi, 2021 This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches.
Ferra Yanuar +4 more
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Bayesian quantile regression [PDF]
, 2006 Recent work by Schennach(2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, θ) = 0 where θ is a k dimensional parameter of interest and k ...
Tony Lancaster, Sung Jae Jun
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Mixtures of quantile regressions [PDF]
Computational Statistics & Data Analysis, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Qiang, Yao, Weixin
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Comparative Economic Research, 2012 In a number of applications, a crucial problem consists in describing and analyzing the influence of a vector Xi of covariates on some real-valued response variable Yi.
Grażyna Trzpiot
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Parametric Elliptical Regression Quantiles
Revstat Statistical Journal, 2020 The article extends linear and nonlinear quantile regression to the case of vector responses by generalizing multivariate elliptical quantiles to a regression context.
Daniel Hlubinka , Miroslav Šiman
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Nonparametric Smoothing for Extremal Quantile Regression with Heavy Tailed Data
Revstat Statistical Journal, 2021 In several different fields, it is interested in analyzing the upper or lower tail quantile of the underlying distribution rather than mean or center quantile.
Takuma Yoshida
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