• Journal of the Korean Data and Information Science Society
• /
• 제19권4호
• /
• pp.1281-1288
• /
• 2008

The purpose of this study is to consider the efficient methods for introducing the confidence interval. We explain various concepts and approaches about the confidence interval estimation. Computing methods for calculating the efficient confidence interval are suggested.

• Communications for Statistical Applications and Methods
• /
• 제26권4호
• /
• pp.385-410
• /
• 2019

This paper proposes a new class of distribution using the concept of exponentiated of distribution function that provides a more flexible model to the baseline model. It also proposes a new lifetime distribution with different types of hazard rates such as decreasing, increasing and bathtub. After studying some basic statistical properties and parameter estimation procedure in case of complete sample observation, we have studied point and interval estimation procedures in presence of type-II censored samples under a classical as well as Bayesian paradigm. In the Bayesian paradigm, we considered a Gibbs sampler under Metropolis-Hasting for estimation under two different loss functions. After simulation studies, three different real datasets having various nature are considered for showing the suitability of the proposed model.

• Journal of the Korean Data and Information Science Society
• /
• 제24권6호
• /
• pp.1455-1464
• /
• 2013

This paper develops maximum likelihood estimators (MLEs) of unknown parameters in an exponentiated half-logistic distribution based on a progressively type-II censored sample. We obtain approximate confidence intervals for the MLEs by using asymptotic variance and covariance matrices. Using importance sampling, we obtain Bayes estimators and corresponding credible intervals with the highest posterior density and Bayes predictive intervals for unknown parameters based on progressively type-II censored data from an exponentiated half logistic distribution. For illustration purposes, we examine the validity of the proposed estimation method by using real and simulated data.