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.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: In this paper, the idea of Stein's estimator is extended to the general discrete and absolutely continuous exponential families of distributions, where adaptive versions of the estimators are also discussed, and a method of estimating the mean vector of a multinormal distribution, based on order statistics corresponding to the |Xi|'s, which permitted improvement over the usual maximum likelihood estimator, for long-tailed empirical distribution functions.
3 citations
••
TL;DR: In this paper, the possibility of using modified Weibull distributions for estimation and statistical-physics analysis of the reliability of articles is analyzed, and it is proposed that a mixture of distributions (exponential and Weibell distributions) be used.
Abstract: The possibility of using modified Weibull distributions for estimation and statistical-physics analysis of the reliability of articles is analyzed. It is proposed that a mixture of distributions – exponential and Weibull distributions – be used.
3 citations
••
TL;DR: This new model is obtained by compounding the exponential distribution and discrete generalized exponential distribution of a second type, which is a generalization of the geometric distribution.
Abstract: In this paper, the researchers attempt to introduce a new generalization of the exponential distribution. This new model is obtained by compounding the exponential distribution and discrete generalized exponential distribution of a second type, which is a generalization of the geometric distribution. The new introduced model contains the exponential-geometric distribution as a special case. Some basic distributional properties, moments and order statistics of the new model are discussed. Estimation of the parameters is illustrated, using the maximum likelihood method, and the model with a real data set is also examined.
3 citations
••
TL;DR: This paper aims to investigate some optimality properties of the ME estimates, and finds that for n>m, when the exact model can be best approximated by one of an infinite number of unknown PDFs from an n parameter exponential family.
Abstract: Purpose – In many problems involving decision‐making under uncertainty, the underlying probability model is unknown but partial information is available In some approaches to this problem, the available prior information is used to define an appropriate probability model for the system uncertainty through a probability density function When the prior information is available as a finite sequence of moments of the unknown probability density function (PDF) defining the appropriate probability model for the uncertain system, the maximum entropy (ME) method derives a PDF from an exponential family to define an approximate model This paper, aims to investigate some optimality properties of the ME estimatesDesign/methodology/approach – For n>m, when the exact model can be best approximated by one of an infinite number of unknown PDFs from an n parameter exponential family The upper bound of the divergence distance between any PDF from this family and the m parameter exponential family PDF defined by the M
3 citations
•
TL;DR: In this paper, the odd generalized exponential linear failure rate distribution is proposed to model the lifetime of data and some statistical properties of the proposed distribution such as the moments, the quantiles, the median, and the mode are investigated.
Abstract: In this paper we propose a new lifetime model, called the odd generalized exponential linear failure rate distribution. Some statistical properties of the proposed distribution such as the moments, the quantiles, the median, and the mode are investigated. The method of maximum likelihood is used for estimating the model parameters. An applications to real data is carried out to illustrate that the new distribution is more flexible and effective than other popular distributions in modeling lifetime data.
3 citations