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Understanding variable dependence, particularly eliciting their statistical properties given a set of covariates, provides the mathematical foundation in practical operations management such as risk analysis and decision-making given…

Methodology · Statistics 2023-09-06 Yunyun Wang , Tatsushi Oka , Dan Zhu

Binomial data with unknown sizes often appear in biological and medical sciences and are usually overdispersed. All previous methods used parametric models and only considered overdispersion due to the variation of sizes. The proposed…

Statistics Theory · Mathematics 2007-06-13 Wei Zhang

It is well known that generalized models is attracting the attention of researchers in recent times because of their flexibilities. Particularly, the logistic model has been generalized and applied by many authors while the half logistic…

Statistics Theory · Mathematics 2008-12-18 A. K. Olapade

The Dirichlet distribution, also known as multivariate beta, is the most used to analyse frequencies or proportions data. Maximum likelihood is widespread for estimation of Dirichlet's parameters. However, for small sample sizes, the…

Methodology · Statistics 2021-03-04 Vincenzo Gioia , Euloge Clovis Kenne Pagui

The Gumbel model is a very popular statistical model due to its wide applicability for instance in the course of certain survival, environmental, financial or reliability studies. In this work, we have introduced a bimodal generalization of…

Methodology · Statistics 2021-06-22 Cira E. G. Otiniano , Roberto Vila , Pedro C. Brom , Marcelo Bourguignon

Classical statistics deals with determined and precise data analysis. But in reality, there are many cases where the information is not accurate and a degree of impreciseness, uncertainty, incompleteness, and vagueness is observed. In these…

Applications · Statistics 2024-06-27 Mansooreh Razmkhah , Mohammad Arashi , Andriette Bekker , Filipe J. Marques

Statistical modeling of multivariate and spatial extreme events has attracted broad attention in various areas of science. Max-stable distributions and processes are the natural class of models for this purpose, and many parametric families…

Methodology · Statistics 2017-08-09 Clement Dombry , Sebastian Engelke , Marco Oesting

In some applied scenarios, the availability of complete data is restricted, often due to privacy concerns; only aggregated, robust and inefficient statistics derived from the data are made accessible. These robust statistics are not…

Methodology · Statistics 2024-02-23 Antoine Luciano , Christian P. Robert , Robin J. Ryder

In this paper, a new three-parameter lifetime distribution is introduced and many of its standard properties are discussed. These include shape of the probability density function, hazard rate function and its shape, quantile function,…

Methodology · Statistics 2013-08-21 Min Wang

We construct a theoretical model for equilibrium distribution of workers across sectors with different labor productivity, assuming that a sector can accommodate a limited number of workers which depends only on its productivity. A general…

Statistical Finance · Quantitative Finance 2012-05-14 Hideaki Aoyama , Hiroshi Iyetomi , Hiroshi Yoshikawa

The characteristic function of the folded normal distribution and its moment function are derived. The entropy of the folded normal distribution and the Kullback--Leibler from the normal and half normal distributions are approximated using…

Methodology · Statistics 2014-02-17 Michail Tsagris , Christina Beneki , Hossein Hassani

Engineers and scientists have been collecting and analyzing fatigue data since the 1800s to ensure the reliability of life-critical structures. Applications include (but are not limited to) bridges, building structures, aircraft and…

Distribution shifts are ubiquitous in real-world machine learning applications, posing a challenge to the generalization of models trained on one data distribution to another. We focus on scenarios where data distributions vary across…

Machine Learning · Statistics 2024-06-05 Steven Wilkins-Reeves , Xu Chen , Qi Ma , Christine Agarwal , Aude Hofleitner

In this paper, we introduce a bivariate exponentaited generalized Weibull-Gompertz distribution. The model introduced here is of Marshall-Olkin type. Several properties are studied such as bivariate probability density function and it is…

Statistics Theory · Mathematics 2015-01-19 M. A. EL-Damcese , Abdelfattah Mustafa , M. S. Eliwa

In this paper we propose an objective Bayesian estimation approach for the parameters of the generalized gamma distribution. Various reference priors are obtained, but showing that they lead to improper posterior distributions. We overcome…

Methodology · Statistics 2014-12-19 Pedro L. Ramos , Francisco Louzada

In this paper, we introduce a new distribution called Burr III-Weibull(BW) distribution using the concept of competing risk. We derive moments, conditional moments, mean deviation and quantiles of the proposed distribution. Also the Renyi's…

Statistics Theory · Mathematics 2021-03-08 G S Deepthy , Nicy Sebastian , Reshma Rison

Cold standby 1-out-of-n redundant systems are well-established models in system reliability engineering. To date, reliability analyses of such systems have predominantly assumed exponential, Erlang, or Weibull failure distributions for…

Applications · Statistics 2025-12-30 Afshin Yaghoubi , Esmaile Khorram , Omid Naghshineh Arjmand

The new method is proposed to monitor the level of currently accumulated fatigue and estimate it by the several statistical methods. The experimental software application was developed and used to get data from sensors (accelerometer, GPS,…

Computational Engineering, Finance, and Science · Computer Science 2016-05-18 Nikita Gordienko

The maximum likelihood principle is widely used in statistics, and the associated estimators often display good properties. indeed maximum likelihood estimators are guaranteed to be asymptotically efficient under mild conditions. However in…

Statistics Theory · Mathematics 2016-12-01 Christophe Culan , Claude Adnet

For two vast families of mixture distributions and a given prior, we provide unified representations of posterior and predictive distributions. Model applications presented include bivariate mixtures of Gamma distributions labelled as…

Statistics Theory · Mathematics 2020-09-09 Aziz LMoudden , Éric Marchand
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