Browsing by Author "Aakriti Pandey"

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Bayesian estimation for inverse weibull distribution under progressive type-II censored data with beta-binomial removals
(Austrian Statistical Society, 2018) Pradeep K. Vishwakarma; Arun Kaushik; Aakriti Pandey; Umesh Singh; Sanjay K. Singh
This paper deals with the estimation procedure for inverse Weibull distribution under progressive type-II censored samples when removals follow Beta-binomial probability law. To estimate the unknown parameters, the maximum likelihood and Bayes estimators are obtained under progressive censoring scheme mentioned above. Bayes estimates are obtained using Markov chain Monte Carlo (MCMC) technique considering square error loss function and compared with the corresponding MLE’s. Further, the expected total time on test is obtained under considered censoring scheme. Finally, a real data set has been analysed to check the validity of the study. © 2018, Austrian Statistical Society. All rights reserved.
Estimations of the parameters of generalised exponential distribution under progressive interval type-I censoring scheme with random removals
(Austrian Statistical Society, 2017) Arun Kaushik; Aakriti Pandey; Sandeep K. Maurya; Umesh Singh; Sanjay K. Singh
The present article aims to point and interval estimation of the parameters of generalised exponential distribution (GED) under progressive interval type-I (PITI) censoring scheme with random removals. The considered censoring scheme is most useful in those cases where continuous examination is not possible. Maximum likelihood, expectationmaximization and Bayesian procedures have been developed for the estimation of parameters of the GED, based on a PITI censored sample. Real datasets have been considered to illustrate the applicability of the proposed work. Further, we have compared the performances of the proposed estimators under PITI censoring to that of the complete sample. © 2017, Austrian Statistical Society. All rights reserved.
On the estimation problems for exponentiated exponential distribution under generalized progressive hybrid censoring
(Austrian Statistical Society, 2021) Aakriti Pandey; Arun Kaushik; Sanjay K. Singh; Umesh Singh
In this article, we considered the statistical inference for the unknown parameters of exponentiated exponential distribution based on a generalized progressive hybrid censored sample under classical paradigm. We have obtained maximum likelihood estimators of the unknown parameters and confidence intervals utilizing asymptotic theory. Entropy measures, such as Shannon entropy and Awad sub-entropy, have been obtained to measure loss of information owing to censoring. Further, the expected total time of the test and expected number of failures, which are useful during the execution of an experiment, also have been computed. The performance of the estimators have been discussed based on mean squared errors. Moreover, the effect of choice of parameters, termination time T, and m on the ETTT and ETNFs also have been observed. For illustrating the proposed methodology, a real data set is considered. © 2021, Austrian Statistical Society. All rights reserved.
Statistical analysis for generalized progressive hybrid censored data from lindley distribution under step-stress partially accelerated life test model
(Austrian Statistical Society, 2021) Aakriti Pandey; Arun Kaushik; Sanjay K. Singh; Umesh Singh
The aim of this paper is to present the estimation procedure for the step-stress partially accelerated life test model under the generalized progressive hybrid censoring scheme. The uncertainties are assumed to be governed by Lindley distribution. The problem with point and interval estimation of the parameters as well as the acceleration factor using maximum likelihood approach for the step-stress partially accelerated life test model has been considered. A simulation study is conducted to monitor the performance of the estimators on the basis of the mean squared error under the considered censoring scheme. The expected total time of the test under an accelerated condition is computed to examine the effects of the parameters on the duration of the test. In addition, a graph of the expected total time of the test under accelerated and un-accelerated conditions is provided to highlight the effect due to acceleration. One real data set has been analyzed for illustrative purposes. © 2021, Austrian Statistical Society. All rights reserved.