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The paper addresses the problem of estimation of the model parameters of the logistic exponential distribution based on progressive type-I hybrid censored sample. The maximum likelihood estimates are obtained and computed numerically using…

Applications · Statistics 2023-02-13 Subhankar Dutta , Suchandan Kayal

In this paper, we introduce a new three-parameter distribution based on the combination of re-parametrization of the so-called EGNB2 and transmuted exponential distributions. This combination aims to modify the transmuted exponential…

Statistics Theory · Mathematics 2020-02-11 Christophe Chesneau , Hassan S. Bakouch , Muhammad Nauman Khan

In this paper we consider Bayesian estimation for the parameters of inverse Gaussian distribution. Our emphasis is on Markov Chain Monte Carlo methods. We provide complete implementation of the Gibbs sampler algorithm. Assuming an…

Methodology · Statistics 2012-10-17 B. N. Pandey , Pulastya Bandyopadhyay

Estimation of reliability and hazard rate is one of the most important problems raised in many applications especially in engineering studies as well as human lifetime. In this regard, different methods of estimation have been used. Each…

Methodology · Statistics 2021-07-27 Hassan Piriaeia , Omid Shojaee

In numerous instances, the generalized exponential distribution can be used as an alternative to the most widely used non-regular family of distributions: Weibull, gamma, lognormal with three-parameters when analyzing lifetime or any skewed…

Methodology · Statistics 2026-03-03 Kiran Prajapat , Sharmishtha Mitra , Debasis Kundu

In this paper, we introduce a new distribution generated by Lindley random variable which offers a more flexible model for modelling lifetime data. Various statistical properties like distribution function, survival function, moments,…

Applications · Statistics 2016-11-25 Deepesh Bhati , Mohd. Aamir Malik

The exponential and the Lomax distributions are widely used in life testing experiments in mixture models. A mixture model of exponential distribution and Lomax distribution is proposed. Parameters of the proposed model are estimated using…

Methodology · Statistics 2020-02-27 Maqsood Ali , Abdul Haq , Muhammad Aslam

A doubly type-II censored scheme is an important sampling scheme in the life testing experiment and reliability engineering. In the present commutation, we have considered estimating ordered scale parameters of two exponential distributions…

Statistics Theory · Mathematics 2024-12-09 Shrajal Bajpai , Lakshmi Kanta Patra

The multivariate extended skew-normal distribution allows for accommodating raw data which are skewed and heavy tailed, and has at least three appealing statistical properties, namely closure under conditioning, affine transformations, and…

Methodology · Statistics 2015-06-19 Mathieu Gerber , Florian Pelgrin

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…

Statistics Theory · Mathematics 2012-03-02 Akram Kohansal , Saeid Rezakhah

Nested error regression models are useful tools for analysis of grouped data, especially in the case of small area estimation. This paper suggests a nested error regression model using uncertain random effects in which the random effect in…

Methodology · Statistics 2017-02-28 Shonosuke Sugasawa , Tatsuya Kubokawa

This article aims to introduced a new lifetime distribution named as exponentiated xgamma distribution (EXGD). The new generalization obtained from xgamma distribution, a special finite mixture of exponential and gamma distributions. The…

Statistics Theory · Mathematics 2018-10-22 Abhimanyu Singh Yadav , Mahendra Saha , Harsh Tripathi , Sumit Kumar

As for other latent-variable problems, exact Bayesian analysis is typically not practicable for mixture problems and approximate methods have been developed. Variational Bayes tends to produce approximate posterior distributions for…

Statistics Theory · Mathematics 2026-02-24 Nils Lid Hjort , Mike Titterington

The Bayesian predictive density has complex representation and does not belong to any finite-dimensional statistical model except for in limited situations. In this paper, we introduce its simple approximate representation employing its…

Statistics Theory · Mathematics 2020-10-30 Michiko Okudo , Fumiyasu Komaki

Models with intractable likelihood functions arise in areas including network analysis and spatial statistics, especially those involving Gibbs random fields. Posterior parameter es timation in these settings is termed a doubly-intractable…

Computation · Statistics 2018-10-16 Lampros Bouranis , Nial Friel , Florian Maire

Exponential random graph models are an important tool in the statistical analysis of data. However, Bayesian parameter estimation for these models is extremely challenging, since evaluation of the posterior distribution typically involves…

Computation · Statistics 2017-05-05 Lampros Bouranis , Nial Friel , Florian Maire

This paper proposes a Bayesian method for estimating the parameters of a normal distribution when only limited summary statistics (sample mean, minimum, maximum, and sample size) are available. To estimate the parameters of a normal…

Methodology · Statistics 2024-11-21 Tomoki Matsumoto

In this article, the exponentiated discrete Lindley distribution is presented and studied. Some important distributional properties are discussed. Using the maximum likelihood method, estimation of the model parameters is investigated.…

Statistics Theory · Mathematics 2018-07-27 M. El-Morshedy , M. S. Eliwa , H. Nagy

In this paper, we present the Bayesian inference procedures for the parameters of the multivariate random effects model derived under the assumption of an elliptically contoured distribution when the Berger and Bernardo reference and the…

Methodology · Statistics 2023-05-26 Olha Bodnar , Taras Bodnar

We present a multi-fidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution. In the Bayesian inference setting, traditional…

Machine Learning · Computer Science 2025-04-03 Caroline Tatsuoka , Minglei Yang , Dongbin Xiu , Guannan Zhang
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