Journal of Advanced Mathematical Modeling

Journal of Advanced Mathematical Modeling

Quantile Regression Based on the Two-Piece Scale Mixtures of Normal

Document Type : Original Paper

Authors
Mohsen Maleki 1
Ali Reza Nematollahi 2
1 Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan
2 Faculty of Science, Department of Statistics, Shiraz University, Shiraz.
10.22055/jamm.2025.49260.2362
Abstract
Regression models sometimes cannot be properly fitted to the data due to the presence of outlying observations and their asymmetric behavior, so using symmetric-asymmetric light-heavy tail models in their structure can solve the above problems. In this article, a robust quantile regression with distributed errors for two-piece scale mixture of normal (Malaki et al. 2019) is introduced to solve the above problems and the models are shown with correct visual designs. The two-piece scale mixture of normal family includes two-piece normal, two-piece t, two-piece slash, and two-piece contaminated normal distributions, the first of which is light-tailed and the rest are heavy-tailed, and its hierarchical structure is such that it enables the use of the expectation-maximization (EM) algorithm in the quantile regression model. We will use this family for modeling and estimation in a real data set and will also provide an index of model goodness of fit.
Keywords
Quantile regression
expectation-maximization algorithm
two-piece scale mixture of normal
Subjects
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  • Receive Date 02 April 2025
  • Revise Date 18 August 2025
  • Accept Date 15 November 2025