Quasi Lindley Regression Model Residual Analysis for Biomedical Data

Authors

  • Ahmed Salih Department of Statistics, Wasit University, Iraq
  • Wafaa Jaafar Hussein Department of Statistics, Wasit University, Iraq

DOI:

https://doi.org/10.19139/soic-2310-5070-2649

Keywords:

Quasi Lindley distribution, Quasi Lindley regression Model, Residual analysis, Martingale residual

Abstract

The current study proposes and presents a new regression model for the response variable following the Quasi Lindley Regression. The unknown parameters of the regression model are estimated using the maximum likelihood method. A simulation study is conducted to evaluate the performance of the maximum likelihood estimates (MLEs). In addition, a residual analysis is performed for the proposed regression model. The log- Quasi Lindley Regression model is compared to several other models, including Lindley regression and gamma regression, using various statistical criteria. The results show that the suggested model fits the data better than these other models. The model is expected to have applications in fields such as economics, biological studies, mortality and recovery rates, health, hazards, measuring sciences, medicine, and engineering.

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Published

2025-06-12

Issue

Vol. 14 No. 2 (2025)

Section

Research Articles

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How to Cite

Quasi Lindley Regression Model Residual Analysis for Biomedical Data. (2025). Statistics, Optimization & Information Computing, 14(2), 956-969. https://doi.org/10.19139/soic-2310-5070-2649