The core of the nonparametric/semiparametric Bayesian analysis is to relax the particular parametric assumptions on the distributions of interest to be unknown and random,and assign them a prior.Selecting a suitable p...The core of the nonparametric/semiparametric Bayesian analysis is to relax the particular parametric assumptions on the distributions of interest to be unknown and random,and assign them a prior.Selecting a suitable prior therefore is especially critical in the nonparametric Bayesian fitting.As the distribution of distribution,Dirichlet process(DP)is the most appreciated nonparametric prior due to its nice theoretical proprieties,modeling flexibility and computational feasibility.In this paper,we review and summarize some developments of DP during the past decades.Our focus is mainly concentrated upon its theoretical properties,various extensions,statistical modeling and applications to the latent variable models.展开更多
This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries:Canada,Australia,and New Zealan...This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries:Canada,Australia,and New Zealand.We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index.The mixing weights follow a set of Probit stick-breaking priors that are time-varying.We find that exchange rates have a positive predictive effect in general,but accounting for time variation does not improve forecasting performance.By contrast,the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases,which is important in forecasting both the mean and the density of commodity prices one period ahead.The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.11471161)the Technological Innovation Item in Jiangsu Province(No.BK2008156).
文摘The core of the nonparametric/semiparametric Bayesian analysis is to relax the particular parametric assumptions on the distributions of interest to be unknown and random,and assign them a prior.Selecting a suitable prior therefore is especially critical in the nonparametric Bayesian fitting.As the distribution of distribution,Dirichlet process(DP)is the most appreciated nonparametric prior due to its nice theoretical proprieties,modeling flexibility and computational feasibility.In this paper,we review and summarize some developments of DP during the past decades.Our focus is mainly concentrated upon its theoretical properties,various extensions,statistical modeling and applications to the latent variable models.
基金The author acknowledges financial support from the National Natural Science Foundation of China(NSFC,No.71773069).
文摘This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries:Canada,Australia,and New Zealand.We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index.The mixing weights follow a set of Probit stick-breaking priors that are time-varying.We find that exchange rates have a positive predictive effect in general,but accounting for time variation does not improve forecasting performance.By contrast,the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases,which is important in forecasting both the mean and the density of commodity prices one period ahead.The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.
