Journal of Advanced Mathematical Modeling

Journal of Advanced Mathematical Modeling

Genetic Modeling of Time to Obesity Event of Mice Using Frequentist and Bayesian Regression Models

Document Type : Original Paper

Authors
Department of Statistics, School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran
Abstract
This paper examines the statistical relationship between the single-nucleotide polymorphisms of various mice and their weight, collected over sixteen weeks, using regression models. The primary concern of this paper is the time-to-event analysis, which is of interest to researchers in survival analysis. Indeed, the time-to-event (fatness) is modeled as a function of the genes as the covariates in this paper. Considering the high dimension of the problem and the high number of ineffective genes on the fatness event, variable selection and sparse modeling are also studied. The frequentist Cox-LASSO regression and the Bayesian Weibull regression models are introduced and compared in this paper. The simulation study and real data analysis show that the Bayesian model performs better than the frequentist method.
Keywords
Subjects

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  • Receive Date 20 October 2024
  • Revise Date 24 September 2025
  • Accept Date 24 January 2026