Quasi Lindley Regression Model Residual Analysis for Biomedical Data

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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Publication Details

Journal
Statistics Optimization & Information Computing
Published
2025-06-12
DOI
https://doi.org/10.19139/soic-2310-5070-2649
Primary Topic
Advanced Statistical Methods and Models
Type
article
Field-Weighted Citation Impact
0.00

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article

Quasi Lindley Regression Model Residual Analysis for Biomedical Data

Ahmed Salih, Wafaa Jaafar Hussein
Statistics Optimization & Information Computing
Advanced Statistical Methods and Models
article

Quasi Lindley Regression Model Residual Analysis for Biomedical Data

Ahmed Salih, Wafaa Jaafar Hussein
article en

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.

Statistics Optimization & Information ComputingVol. 14(2)
University of Wasit (IQ)
Đại học Quốc gia Hà Nội
Openalex Percentile: Top 81%
Advanced Statistical Methods and Models
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Quasi Lindley Regression Model Residual Analysis for Biomedical Data — Ahmed Salih, Wafaa Jaafar Hussein · Statistics Optimization & Information Computing (2025) | TGRS Research Map | TGRS