arXiv subjects

Akram Kohansal

Publications and source records attributed to Akram Kohansal.

3 recordsLinked to original sources

arXiv2017

Inference of $R=P(Y<X)$ for two-parameter Rayleigh distribution based on progressively censored samples

Based on independent progressively Type-II censored samples from two-parameter Rayleigh distributions with the same location parameter but different scale parameters, the UMVUE and maximum likelihood estimator of $R=P(Y<X)$ are obtained. Also the exact, asymptotic and bootstrap confidence intervals for $R$ are evaluated. Using Gibbs {sampling,} the Bayes estimator and corresponding credible interval for $R$ are obtained too. Applying Monte Carlo {simulations,} we compare the performances of the different estimation methods. Finally we make use of simulated data and two real data sets to show the competitive performance of our method.

stat.AP↗

arXiv2013

Testing Exponentiality Based on Rényi Entropy With Progressively Type-II Censored Data

We express the joint Rényi entropy of progressively censored order statistics in terms of an incomplete integral of the hazard function, and provide a simple estimate of the joint Rényi entropy of progressively Type-II censored data. Then we establish a goodness of fit test statistic based on the Rényi Kullback-Leibler information with the progressively Type-II censored data, and compare its performance with the leading test statistic. A Monte Carlo simulation study shows that the proposed test statistic shows better powers than the leading test statistic against the alternatives with monotone increasing, monotone decreasing and nonmonotone hazard functions.

stat.ME↗

arXiv2012

Parameter Estimation of Type-II Hybrid Censored Weighted Exponential Distribution

A hybrid censoring scheme is a mixture of Type-I and Type-II censoring schemes. We study the estimation of parameters of weighted exponential distribution based on Type-II hybrid censored data. By applying EM algorithm, maximum likelihood estimators are evaluated. Also using Fisher infirmation matrix asymptotic confidence intervals are provided. By applying Markov Chain Monte Carlo techniques Bayes estimators, and corresponding highest posterior density confidence intervals of parameters are obtained. Monte Carlo simulations to compare the performances of the different methods is performed and one data set is analyzed for illustrative purposes.

math.ST↗