Robust Asymmetric Partially Linear Model for Censored and Outlier Contaminated Data

Authors

  • Omolbanin Moatani Department of Statistics, Marv.C., Islamic Azad University, Marvdasht, Iran
  • Karim Zare Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
  • Mohsen Maleki Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan, 81746-73441, Iran
  • Darren Wraith Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology (QUT), Queensland, Australia

Abstract

This study introduces a robust partially linear modeling framework for censored observations based on the two-piece scale mixture of normal (TP-SMN) class. Using this distributional family enables exact maximum likelihood inference while simultaneously accommodating skewness, the presence of outliers, and censoring mechanisms. The effectiveness of the proposed methodology is assessed through comprehensive simulation experiments and an empirical analysis using real
data, which indicate superior estimation precision and enhanced computational performance. These results highlight the advantages of TPSMNPLMs for modeling censored data with symmetric and asymmetric, as well as light- and heavy-tailed, behavior, offering both methodological flexibility and practical applicability.

Published

2026-07-06

Issue

Vol. 20

Section

Vol. 20, No. 4, (2026)

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