scholarly journals Vine copula approximation: a generic method for coping with conditional dependence

2017 ◽  
Vol 28 (1) ◽  
pp. 219-237 ◽  
Author(s):  
Mimi Zhang ◽  
Tim Bedford
Keyword(s):  
Conditional Dependence ◽  
Vine Copula

scholarly journals Modeling the Conditional Dependence between Discrete and Continuous Random Variables with Applications in Insurance

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 45
Author(s):  
Emilio Gómez-Déniz ◽  
Enrique Calderín-Ojeda
Keyword(s):  
Structural Model ◽  
Medical Expenditure Panel Survey ◽  
Health Care Use ◽  
Medical Expenditure ◽  
Panel Survey ◽  
Conditional Distributions ◽  
Conditional Dependence ◽  
Outpatient Visits ◽  
Conditional Expectations ◽  
The U.S

We jointly model amount of expenditure for outpatient visits and number of outpatient visits by considering both dependence and simultaneity by proposing a bivariate structural model that describes both variables, specified in terms of their conditional distributions. For that reason, we assume that the conditional expectation of expenditure for outpatient visits with respect to the number of outpatient visits and also, the number of outpatient visits expectation with respect to the expenditure for outpatient visits is related by taking a linear relationship for these conditional expectations. Furthermore, one of the conditional distributions obtained in our study is used to derive Bayesian premiums which take into account both the number of claims and the size of the correspondent claims. Our proposal is illustrated with a numerical example based on data of health care use taken from Medical Expenditure Panel Survey (MEPS), conducted by the U.S. Agency of Health Research and Quality.

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.

Sign in / Sign up

Export Citation Format

Share Document