Results 81 to 90 of about 1,401 (188)

Local dependence in bivariate copulae with Beta marginals

Revista Colombiana de Estadística, 2017
The local dependence function (LDF) describes changes in the correlation structure of continuous bivariate random variables along their range. Bivariate density functions with Beta marginals can be used to model jointly a wide variety of data with bounded outcomes in the (0,1) range, e.g. proportions.
Eirini Koutoumanou, Angie Wade, Mario Cortina-Borja   +2 more
openaire   +6 more sources

Poisson count time series

Journal of Time Series Analysis, Volume 47, Issue 2, Page 279-303, March 2026.
This article reviews and compares popular methods, some old and some recent, that produce time series having Poisson marginal distributions. The article begins by narrating ways where time series with Poisson marginal distributions can be produced.
Jiajie Kong, Robert Lund
wiley   +1 more source

Modelling cascading effects for systemic risk: Properties of the Freund copula

Dependence Modeling, 2019
We consider a dependent lifetime model for systemic risk, whose basic idea was for the first time presented by Freund. This model allows to model cascading effects of defaults for arbitrarily many economic agents.
Guzmics Sándor, Pflug Georg Ch.
doaj   +1 more source

Bivariate copula regression models for semi-competing risks. [PDF]

Stat Methods Med Res, 2023
Wei Y, Wojtyś M, Sorrell L, Rowe P.
europepmc   +1 more source

Bivariate power Lomax Sarmanov distribution: Statistical properties, Reliability measures, and Parameter estimation

Alexandria Engineering Journal
This paper presents a bivariate power Lomax Sarmanov distribution (BPL-SARD) constructed from Sarmanov copulas and power Lomax (PL) marginal distributions.
M.A. Abd Elgawad, M.A. Alawady, H.M. Barakat, G.M. Mansour, I.A. Husseiny, Salem A. Alyami, Atef F. Hashem, M.O. Mohamed   +7 more
doaj   +1 more source

A Copula Discretization of Time Series-Type Model for Examining Climate Data

Mathematics
The study presents a comparative analysis of climate data under two scenarios: a Gaussian copula marginal regression model for count time series data and a copula-based bivariate count time series model.
Dimuthu Fernando, Olivia Atutey, Norou Diawara   +2 more
doaj   +1 more source

Bivariate Flood Frequency Analysis Using the Copula Functions [PDF]

علوم و مهندسی آبیاری, 2015
In the conventional methods of flood frequency analysis, the flood peak variable is just considered and assumed that this variable follows some specific parametric distribution function.
meysam salari, Ali Mohammad Akhond Ali, Arash Adib, Ali Reza Dneshkhah   +3 more
doaj  

Bayesian Inference for Copula-Linked Bivariate Generalized Exponential Distributions: A Comparative Approach

Axioms
This paper addresses the limitations of existing bivariate generalized exponential (GE) distributions for modeling lifetime data, which often exhibit rigid dependence structures or non-GE marginals.
Carlos A. dos Santos, Saralees Nadarajah, Fernando A. Moala, Hassan S. Bakouch, Shuhrah Alghamdi   +4 more
doaj   +1 more source

Robust Estimation of Bivariate Copulas

, 2013
Copula functions are very convenient for modelling multivariate observations. Popular es- timation methods are the two-stage maximum likelihood and an alternative semi-parametric with empirical cumulative distribution functions (cdf) for the margins.
Guerrier, Stéphane, Orso, Samuel, Victoria-Feser, Maria-Pia   +2 more
openaire   +1 more source

A method to select bivariate copula functions

, 2019
Summary: Copula functions have been extensively used in applied statistics, becoming a good alternative for modeling the dependence of multivariate data. Each copula function has a different dependence structure. An important issue in these applications is the choice of an appropriate copula function model for each case where standard classical or ...
Tovar Cuevas, José Rafael, Portilla Yela, Jennyfer, Achcar, Jorge Alberto   +2 more
openaire   +2 more sources