Sanjeev K. Tomer

Estimation procedures for Maxwell distribution under type-I progressive hybrid censoring scheme

The Maxwell (or Maxwell–Boltzmann) distribution was invented to solve the problems relating to physics and chemistry. It has also proved its strength of analysing the lifetime data. For this distribution, we consider point and interval estimation procedures in the presence of type-I progressively hybrid censored data. We obtain maximum likelihood estimator of the parameter and provide asymptotic and bootstrap confidence intervals of it. The Bayes estimates and Bayesian credible and highest posterior density intervals are obtained using inverted gamma prior. The expression of the expected number of failures in life testing experiment is also derived. The results are illustrated through the simulation study and analysis of a real data set is presented.

Bayesian analysis of masked series system lifetime data from a family of lifetime distributions

We consider the Bayesian analysis of lifetime data obtained from multi-component series systems when lifetime of each component of the system follows a ‘family of lifetime distributions’. We obtain Bayes estimates of parameters included in this family using incomplete system lifetime data under competing risk model. In order to show the applicability of results to the real life problems, we present analysis of a real data set of electric appliances in which failures occur due to several causes. We also give an ad-hoc technique to generate sample observations from a complicated distribution.

Maximum Likelihood Estimation of Component Reliability Using Masked Series System Lifetime Data

We consider the estimation of component reliability measures using incomplete series system life test data when the lifetime of each component follows a ‘family of lifetime distributions’ which covers several distributions as special cases. Using maximum likelihood approach, ML estimates and asymptotic confidence intervals for the parameters included in that family are obtained. The performance of estimators is illustrated by simulation study.

Estimation of stress–strength reliability for Maxwell distribution under progressive type-II censoring scheme

This paper deals with the estimation of stress–strength reliability

Robust Bayesian analysis of Weibull failure model

P=P[Y<X]

Reliability Estimation for Lindley Distribution under Type-I Progressive Hybrid Censoring Scheme

P=P[Y<X], when the strength X and stress Y both follow Maxwell distribution with different parameters. We obtain maximum likelihood and Bayes estimates of P using progressive type-II censored samples. We also provide procedures to evaluate asymptotic and bootstrap confidential intervals, as well as, Bayesian credible and highest posterior density intervals for P. We present simulation study and analyze a real data set for numerical illustrations.

Reliability Estimation for Negative Binomial Distribution Under Type-II Censoring Scheme

Abstract In this paper we consider the estimation of R = P[Y < X], where both the random variables X and Y follow Maxwell distributions with different scale parameters. We obtain the maximum likelihood estimator of R. We also provide asymptotic confidence intervals and bootstrap intervals for the same. The Bayes estimator of R is derived under square error loss function and Bayesian credible and HPD intervals are also obtained. The results are illustrated through simulation study and analysis of a real data set.