A Bayesian Approach to Robust Skewed Autoregressive Processes. Issue 2 (November 2017)

• Record Type: Journal Article
• Authors: Maleki, Mohsen; Arellano-Valle, Reinaldo B.; Dey, Dipak K.; Mahmoudi, Mohammad R.; Jalali, Seyed Mohammad J.
• Is Part Of: Calcutta Statistical Association bulletin. Volume 69:Issue 2(2017:Nov.)
• Page Start: 165
• Page End: 182
• Publication Date: 2017-11
• Subjects: Autoregressive models -- Gibbs sampling -- MCMC method -- non-Gaussian time series -- SMSN distributions; Mathematical statistics -- Periodicals; Statistics -- Methodology -- Periodicals; Statistics -- Periodicals; 519.5
• DOI: 10.1177/0008068317732196
• Languages: English
• ISSNs: 0008-0683

Abstract

Abstract: This article studies autoregressive (AR) models assuming innovations with scale mixtures of skew-normal (SMSN) distributions, an attractive and flexible family of probability distributions. A Bayesian analysis considering informative prior distributions is presented. Comprehensive simulation studies are performed to support the performance of the proposed model and methods. The proposed methods are also applied on a real-time series data which has previously been analysed under Gaussian and Student- t AR models.