Residual analysis for Minimum Guarantee transformation regression model with application
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Abstract
This paper proposes a new regression model in which the response variable follows the Minimum Guarantee Akash distribution. The model parameters are estimated using the maximum likelihood estimation method, and a Monte Carlo simulation study is performed to investigate the finite-sample behavior of the estimators in terms of bias, mean square error, and consistency. Model adequacy is evaluated through residual diagnostics, while influential observations are detected using Cook’s distance. The proposed Minimum Guarantee Akash regression model is further compared with Weibull and Gamma regression models using common goodness-of-fit measures such as AIC, BIC, and HQIC. The findings demonstrate that the Minimum Guarantee Akash regression model provides a better fit and greater flexibility for modeling lifetime and reliability data. Owing to its adaptability, the model can be effectively applied in economics, biological sciences, survival analysis, public health, engineering reliability, and risk assessment.
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