Results 1 to 10 of about 428 (69)

On partially Schur-constant models and their associated copulas

open access: yesDependence Modeling, 2021
Schur-constant vectors are used to model duration phenomena in various areas of economics and statistics. They form a particular class of exchangeable vectors and, as such, rely on a strong property of symmetry.
Lefèvre Claude
doaj   +1 more source

A study of minimax shrinkage estimators dominating the James-Stein estimator under the balanced loss function

open access: yesOpen Mathematics, 2022
One of the most common challenges in multivariate statistical analysis is estimating the mean parameters. A well-known approach of estimating the mean parameters is the maximum likelihood estimator (MLE).
Benkhaled Abdelkader   +4 more
doaj   +1 more source

Multiple inflated negative binomial regression for correlated multivariate count data

open access: yesDependence Modeling, 2022
This article aims to provide a method of regression for multivariate multiple inflated count responses assuming the responses follow a negative binomial distribution.
Mathews Joseph   +3 more
doaj   +1 more source

Evaluating early pandemic response through length-of-stay analysis of case logs and epidemiological modeling: A case study of Singapore in early 2020

open access: yesComputational and Mathematical Biophysics, 2023
It is now known that early government interventions in pandemic management helps in slowing down the pandemic in the initial phase, during which a conservative basic reproduction number can be maintained.
Sreevalsan-Nair Jaya   +4 more
doaj   +1 more source

A new extension of the two-parameter bathtub hazard shaped distribution

open access: yesScientific African, 2022
The need of new life time distributions that can be used to fit real data sets is crucial in lifetime data analysis. This article uses the two parameter bathtub (TPBT) and the generalized exponential (GE) distributions to propose a new family of lifetime
Ammar M. Sarhan, Abdelfattah Mustafa
doaj   +1 more source

A combinatorial proof of the Gaussian product inequality beyond the MTP2 case

open access: yesDependence Modeling, 2022
A combinatorial proof of the Gaussian product inequality (GPI) is given under the assumption that each component of a centered Gaussian random vector X=(X1,…,Xd){\boldsymbol{X}}=\left({X}_{1},\ldots ,{X}_{d}) of arbitrary length can be written as a ...
Genest Christian, Ouimet Frédéric
doaj   +1 more source

On shrinkage estimators improving the positive part of James-Stein estimator

open access: yesDemonstratio Mathematica, 2021
In this work, we study the estimation of the multivariate normal mean by different classes of shrinkage estimators. The risk associated with the quadratic loss function is used to compare two estimators. We start by considering a class of estimators that
Hamdaoui Abdenour
doaj   +1 more source

Asymptotic behavior of the maximum of multivariate order statistics in a norm sense

open access: yes, 2020
In this work, we investigate the asymptotic behavior of the extremes of a multivariate data by using the Reduced Ordering Principle (R-ordering). When, the sup-norm is used, we reveal the interrelation between the R-ordering principle and Marginal ...
H. M. Barakat, E. Nigm, M. H. Harpy
semanticscholar   +1 more source

EXTENDED FRACTIONAL INTEGRALS, PRODUCTS AND RATIOS OF MATRICES AND STATISTICAL DISTRIBUTIONS

open access: yes, 2020
For two functions, Mellin convolutions of products and ratios are well-known in the literature. But these for three or more functions is not discussed extensively in the literature.
A. Mathai
semanticscholar   +1 more source

Lorenz-generated bivariate Archimedean copulas

open access: yesDependence Modeling, 2020
A novel generating mechanism for non-strict bivariate Archimedean copulas via the Lorenz curve of a non-negative random variable is proposed. Lorenz curves have been extensively studied in economics and statistics to characterize wealth inequality and ...
Fontanari Andrea   +2 more
doaj   +1 more source

Home - About - Disclaimer - Privacy