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Statistica Sinica 20 (2010), 481-496


Yuzhi Cai

University of Plymouth

Abstract: Multivariate quantiles have been defined by a number of researchers and can be estimated by different methods. However, little work can be found in the literature about Bayesian estimation of joint quantiles of multivariate random variables. In this paper we present a multivariate quantile function model and propose a Bayesian method to estimate the model parameters. The methodology developed here enables us to estimate the multivariate quantile surfaces and the joint probability without direct use of the joint probability distribution or density functions of the random variables of interest. Furthermore, simulation studies and applications of the methodology to bivariate economics data sets show that the method works well both theoretically and practically.

Key words and phrases: Bayesian method, \(\tau\)th quantile surface, \(\tau\)th quantile curve, multivariate quantile function, MCMC, log return.

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