Results 111 to 120 of about 1,034 (200)

Joint Risk Analysis of Extreme Rainfall and High Tide Level Based on Extreme Value Theory in Coastal Area. [PDF]

Int J Environ Res Public Health, 2023
Chen H, Xu Z, Chen J, Liu Y, Li P.
europepmc   +1 more source

Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts. [PDF]

Sci Rep, 2022
Sadeghfam S, Mirahmadi R, Khatibi R, Mirabbasi R, Nadiri AA.   +4 more
europepmc   +1 more source

On Concomitants of Record Values from Generalized Farlie-Gumbel-Morgenstern Distribution [PDF]

Journal of Statistical Science and Application, 2014
null N. A. Mokhlis, null S. K. khames
openaire   +1 more source

Bivariate Length-Biased Exponential Distribution under Progressive Type-II Censoring: Incorporating Random Removal and Applications to Industrial and Computer Science Data

Axioms
In this paper, we address the analysis of bivariate lifetime data from a length-biased exponential distribution observed under Type II progressive censoring with random removals, where the number of units removed at each failure time follows a binomial ...
Aisha Fayomi, Ehab M. Almetwally, Maha E. Qura   +2 more
doaj   +1 more source

Fractional Survival Functional Entropy of Engineering Systems. [PDF]

Entropy (Basel), 2022
Alomani G, Kayid M.
europepmc   +1 more source

Model–based Clustering with Copulas [PDF]

, 2010
Ernst, Dominik
core   +1 more source

Stochastic Frontier Models With Correlated Error Components [PDF]

In the productivity modelling literature, the disturbances U (representing technical inefficiency) and V (representing noise) of the composite error W=V-U of the stochastic frontier model are assumed to be independent random variables.
Murray D Smith
core  

A non symmetric extension of Farlie-Gumbel-Morgenstern distributions

, 2013
A copula is a function that completely describes the dependence structure between the marginal distributions. One of the most important parametric family of copulas is the Farlie-Gumbel-Morgenstern (FGM) family. In practical applications this copula has been shown to be somewhat limited. We propose a new non symmetric extension of this family.
openaire   +1 more source

Burst and Memory-aware Transformer: capturing temporal heterogeneity. [PDF]

Front Comput Neurosci, 2023
Lee B, Lee JH, Lee S, Kim CH.
europepmc   +1 more source

Uni-variate and bi-variate Inverted Exponential Teissier distribution in Bayesian and non-Bayesian framework to model stochastic dynamic variation of climate data. [PDF]

Theor Appl Climatol, 2022
Thakur D, Bhattacharya S, Das I.
europepmc   +1 more source