Economics at your fingertips  

Logistic Quantile Regression for Bounded Outcomes Using a Family of Heavy-Tailed Distributions

Christian E. Galarza, Panpan Zhang and Víctor H. Lachos ()
Additional contact information
Christian E. Galarza: Escuela Superior Politécnica del Litoral
Panpan Zhang: University of Pennsylvania
Víctor H. Lachos: University of Connecticut

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 2, No 6, 325-349

Abstract: Abstract Mean regression model could be inadequate if the probability distribution of the observed responses is not symmetric. Under such situation, the quantile regression turns to be a more robust alternative for accommodating outliers and misspecification of the error distribution, since it characterizes the entire conditional distribution of the outcome variable. This paper proposes a robust logistic quantile regression model by using a logit link function along the EM-based algorithm for maximum likelihood estimation of the p th quantile regression parameters in Galarza (Stat 6, 1, 2017). The aforementioned quantile regression (QR) model is built on a generalized class of skewed distributions which consists of skewed versions of normal, Student’s t, Laplace, contaminated normal, slash, among other heavy-tailed distributions. We evaluate the performance of our proposal to accommodate bounded responses by investigating a synthetic dataset where we consider a full model including categorical and continuous covariates as well as several of its sub-models. For the full model, we compare our proposal with a non-parametric alternative from the so-called quantreg R package. The algorithm is implemented in the R package lqr, providing full estimation and inference for the parameters, automatic selection of best model, as well as simulation of envelope plots which are useful for assessing the goodness-of-fit.

Keywords: Bounded outcomes; Quantile regression model; EM algorithm; Scale mixtures of Normal distributions; Primary 62M10; Secondary 62E10. (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1007/s13571-020-00231-0

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

Page updated 2022-05-12
Handle: RePEc:spr:sankhb:v:83:y:2021:i:2:d:10.1007_s13571-020-00231-0