Logistic quantile regression for bounded outcomes using a family of heavy-tailed distributions

DOI10.1007/s13571-020-00231-0zbMath1493.62522OpenAlexW3039772103MaRDI QIDQ2061768

Panpan Zhang, Christian E. Galarza, Victor Hugo Lachos

Publication date: 21 December 2021

Published in: Sankhyā. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13571-020-00231-0

zbMATH Keywords

EM algorithmscale mixtures of normal distributionsquantile regression modelbounded outcomes

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Characterization and structure theory of statistical distributions (62E10)

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Uses Software

• R
• quantreg
• AS 229

Cites Work

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• Beta Regression for Modelling Rates and Proportions
• Stepwise multiple quantile regression estimation using non-crossing constraints
• Generalized linear models
• Censored regression quantiles
• A quantile parametric mixed regression model for bounded response variables
• The Log-Lindley distribution as an alternative to the beta regression model with applications in insurance
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• Quantile Regression via the EM Algorithm
• Linear Quantile Regression Based on EM Algorithm
• Simultaneous multiple non-crossing quantile regression estimation using kernel constraints
• Bayesian Semiparametric Modelling in Quantile Regression
• Regression Quantiles
• Bayesian Semiparametric Median Regression Modeling
• A generalized class of skew distributions and associated robust quantile regression models
• Power Transformation Toward a Linear Regression Quantile
• Bayesian quantile regression