THERMAL SCIENCE

International Scientific Journal

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Ting Chen

,

Min Xiao

TWO-PART QUANTILE REGRESSION ANALYSIS WITH VARIABLE SELECTION FOR COMPLEX DATA AND ITS APPLICATION

ABSTRACT
Semi-continuous data, also known as zero-inflated non-negative continuous data, are commonly observed in various fields such as biomedicine, environmental science, and ecology. Such data exhibit a combination of zero values and positive continuous values that are right-skewed and heteroscedastic. In this study, we present a novel approach for analyzing complex semi-continuous data using a two-part quantile regression method. In addition, we investigate variable selection techniques using least absolute shrinkage and selection operator, smoothly clipped absolute deviation, and minimax concave penalty methods within the framework of two-part quantile regression. Simulation studies are then conducted to evaluate the effectiveness of the proposed methods. Finally, we apply these methods to examine the determinants of health care spending decisions in American households.
KEYWORDS
semi-continuous distribution, two-part model, quantile regression, variable selection
PAPER SUBMITTED: 2024-03-01
PAPER REVISED: 2024-07-07
PAPER ACCEPTED: 2024-07-07
PUBLISHED ONLINE: 2025-07-06
DOI REFERENCE: https://doi.org/10.2298/TSCI2503023C
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2025, VOLUME 29, ISSUE Issue 3, PAGES [2023 - 2030]
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Two-part quantile regression analysis with variable selection for complex data and its application

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence