Topic
Natural exponential family
About: Natural exponential family is a research topic. Over the lifetime, 1973 publications have been published within this topic receiving 60189 citations. The topic is also known as: NEF.
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TL;DR: In this article, a general identity for the product moments of successive order statistics is given, which is valid in a class of probability distributions including Weibull, Pareto, exponential and Burr distributions.
Abstract: A general identity for the product moments of successive order statistics is given, which is valid in a class of probability distributions including Weibull, Pareto, exponential and Burr distributions.
4 citations
TL;DR: In this paper, the Bayesian prediction intervals (BPI) of future generalized order statistics are obtained under a mixture of two components of generalized exponential distributions in case of one and two-sample schemes.
Abstract: In this paper, the Bayesian prediction intervals (BPI ′s) of future generalized order statistics are obtained under a mixture of two components of generalized exponential distributions in case of one and two-sample schemes. Based on a type-II censored sample from a real data set, the BPI ′s of the remaining observations are obtained. Mathematics Subject Classification: 62F10; 62F15; 62N01; 62N02
4 citations
TL;DR: In this paper, the authors considered the four-parameter bivariate generalized exponential distribution and proposed an expectation-maximization algorithm to find the maximum likelihood estimators of the four parameters under random left censoring.
Abstract: In this paper, we consider the four-parameter bivariate generalized exponential distribution proposed by Kundu and Gupta [Bivariate generalized exponential distribution, J. Multivariate Anal. 100 (2009), pp. 581–593] and propose an expectation–maximization algorithm to find the maximum-likelihood estimators of the four parameters under random left censoring. A numerical experiment is carried out to discuss the properties of the estimators obtained iteratively.
4 citations
TL;DR: In this paper, a general successive substitutions' scheme is developed to estimate parameters in a finite mixture of distributions from the exponential family, based on censored data, assuming that the data can be grouped in the first class and the number of observations in each of the remaining classes are known Examples from Poisson Exponential and Normal distributions are given
Abstract: A general successive substitutions' scheme is developed to estimate parameters in a finite mixture of distributions from the exponential family, based on censored data. It is assumed that the data can be grouped in the first class and the number of observations in each of the remaining classes are known Examples from Poisson Exponential and Normal distributions are given A small simulation exercise has also been carried out for the mixture of two one parameter exponential population.
4 citations
4 citations