A nonparametric Bayesian approach to copula estimation
نویسندگان
چکیده
منابع مشابه
Bayesian Nonparametric Conditional Copula Estimation of Twin Data∗
Several studies on heritability in twins aim at understanding the different contribution of environmental and genetic factors to specific traits. Considering the National Merit Twin Study, our purpose is to correctly analyse the influence of the socioeconomic status on the relationship between twins’ cognitive abilities. Our methodology is based on conditional copulas, which allow us to model t...
متن کاملNonparametric Regression Approach to Bayesian Estimation
Estimation of unknown parameters and functions involved in complex nonlinear econometric models is a very important issue. Existing estimation methods include generalised method of moments (GMM) by Hansen (1982) and others, efficient method of moments (EMM) by Gallant and Tauchen (1997), Markov chain Monte Carlo (MCMC) method by Chernozhukov and Hong (2003), and nonparametric simulated maximum ...
متن کاملNonparametric estimation of the conditional tail copula
The tail copula is widely used to describe the dependence in the tail of multivariate distributions. In some situations such as risk management, the dependence structure may be linked with some covariate. The tail copula thus depends on this covariate and is referred to as the conditional tail copula. The aim of this paper is to propose a nonparametric estimator of the conditional tail copula a...
متن کاملNonparametric copula estimation under bivariate censoring
In this paper, we consider nonparametric copula inference under bivariate censoring. Based on an estimator of the joint cumulative distribution function, we define a discrete and two smooth estimators of the copula. The construction that we propose is valid for a large number of estimators of the distribution function, and therefore for a large number of bivariate censoring frameworks. Under so...
متن کاملNonparametric Bayesian Estimation of a Bivariate Copula Using the Jeffreys Prior SIMON GUILLOTTE and FRANÇOIS PERRON
ABSTRACT. A bivariate distribution with continuous margins can be uniquely decomposed via a copula and its marginal distributions. We consider the problem of estimating the copula function and adopt a nonparametric Bayesian approach. On the space of copula functions, we construct a finite dimensional approximation subspace which is parameterized by a doubly stochastic matrix. A major problem he...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Computation and Simulation
سال: 2018
ISSN: 0094-9655,1563-5163
DOI: 10.1080/00949655.2017.1421194