Bayesian Analysis of Modified Inverse Lomax Distribution with Application
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Page Numbers:
141-155
Digital Object Identifier:
10.58578/mjms.v3i2.5076
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Authors:
Lal Babu Sah Telee1* 
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Abstract
This study investigates the use of the Modified Inverse Lomax (MILX) distribution to model survival data for patients suffering from Head and Neck cancer who were treated with radiotherapy. The dataset, consisting of 44 observations, is analyzed using maximum likelihood estimation (MLE) and Bayesian methods via Markov Chain Monte Carlo (MCMC) sampling. Key parameters of the MILX model are estimated, and posterior predictive checks are performed to assess model fit. Convergence diagnostics using Gelman-Rubin statistics and trace plots demonstrate reliable parameter estimation, with high effective sample sizes. The model's performance is evaluated using posterior predictive intervals (PPI) and Widely Applicable Information Criterion (WAIC). Residual analysis shows that while the model fits most of the data well, it struggles with larger observed values. The findings highlight the applicability of the MILX distribution in modeling heavy-tailed data with varying uncertainties, and its utility in predicting future observations.
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Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
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