Browsing by Author "Vikas Baranwal"

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Parametric inference for an extended illness-death model in the presence of censored data
(Taylor and Francis Ltd., 2025) Vikas Baranwal; Sanjeev K. Tomer
An extended illness-death model is a multistate model that offers an insightful approach to study the progression of successive events over a period of time. The anatomization of such a model includes inference on transition probabilities and transition hazard rate functions. In this paper, we consider an extended illness-death model with Weibull transition hazard and provide maximum likelihood and Bayes point and interval estimates of transition probabilities in the presence of randomly censored time-to-event data. Bayesian estimation is performed under non-informative and subjectively elicited priors. An extensive simulation study is conducted for numerical illustrations. Real data, concerning the study of hospital-acquired infection, are analysed. © 2025 Informa UK Limited, trading as Taylor & Francis Group.
Stress-strength reliability estimation for non-identical strength: a study on power Muth distribution
(Springer, 2025) Prashant Kumar Sonker; Agni Saroj; Vikas Baranwal; Mukesh Sunil Kumar
This article explores the extension of the stress-strength reliability model of a system and the multi-component systems when the components of the system are considered to be non-identical. These components are separated into two categories. Each component of the system has some strength and the common random stress applied to it. The component strength of both the categories follows Power-Muth (PM) distribution and the stress applied to the components also follows PM distribution. It may follow any other lifetime distributions. Both the strength and the stress are independent of each other. The estimation of stress-strength reliability and multi-component stress-strength reliability is carried out using well-known ML and MPS estimation methods. Based on varying parameters, the reliability of the models is discussed. All the statistical calculations are done by using Monte Carlo simulation. Real data applicability of the extended model is also performed in the article. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2025.