scholarly journals Beyond unidimensional poverty analysis using distributional copula models for mixed ordered‐continuous outcomes

2021 ◽  
Author(s):  
Maike Hohberg ◽  
Francesco Donat ◽  
Giampiero Marra ◽  
Thomas Kneib
Keyword(s):  
Copula Models ◽  
Continuous Outcomes

Joint analysis of mixed discrete and continuous outcomes via copula models

2013 ◽  
pp. 139-156 ◽  
Author(s):  
Beilei Wu ◽  
Niroshan Withanage
Keyword(s):  
Joint Analysis ◽  
Copula Models ◽  
Continuous Outcomes

Dynamic Correlation Hedging in Copula Models for Portfolio Selection

2008 ◽  
Author(s):  
Denitsa Stefanova ◽  
Redouane Elkamhi
Keyword(s):  
Portfolio Selection ◽  
Copula Models ◽  
Dynamic Correlation

Semiparametric Estimation for Index Copula Models

2014 ◽  
Author(s):  
Bingduo Yang ◽  
Yuhua Li ◽  
Peiqin Zhang
Keyword(s):  
Semiparametric Estimation ◽  
Copula Models

Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies

2021 ◽  
pp. 1-28
Author(s):  
Wei Zhang ◽  
Colin O. Wu ◽  
Xiaoyang Ma ◽  
Xin Tian ◽  
Qizhai Li
Keyword(s):  
Longitudinal Data ◽  
Copula Models ◽  
Multivariate Longitudinal Data

scholarly journals Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2156
Author(s):  
George Pouliasis ◽  
Gina Alexandra Torres-Alves ◽  
Oswaldo Morales-Napoles
Keyword(s):  
Time Series ◽  
Stochastic Modeling ◽  
Copula Models ◽  
Multiple Processes ◽  
Spatial Dependencies ◽  
Probabilistic Dependence ◽  
Temporal And Spatial ◽  
Vine Copula ◽  
Synthetic Time Series ◽  
Hydroclimatic Variables

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

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