Inference on a progressive type I interval-censored truncated normal distribution

Chandrakant Lodhi and Yogesh Mani Tripathi

Journal of Applied Statistics, 2020, vol. 47, issue 8, 1402-1422

Abstract: In this paper, we consider the problem of making statistical inference for a truncated normal distribution under progressive type I interval censoring. We obtain maximum likelihood estimators of unknown parameters using the expectation-maximization algorithm and in sequel, we also compute corresponding midpoint estimates of parameters. Estimation based on the probability plot method is also considered. Asymptotic confidence intervals of unknown parameters are constructed based on the observed Fisher information matrix. We obtain Bayes estimators of parameters with respect to informative and non-informative prior distributions under squared error and linex loss functions. We compute these estimates using the importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We present a Monte Carlo simulation study to compare the performance of proposed point and interval estimators. Analysis of a real data set is also performed for illustration purposes. Finally, inspection times and optimal censoring plans based on the expected Fisher information matrix are discussed.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:47:y:2020:i:8:p:1402-1422

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DOI: 10.1080/02664763.2019.1679096

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