Additive nonparametric regression with autocorrelated errors

• 成果类型: Article
• 署名作者: Smith, M; Wong, CM; Kohn, R
• 署名单位: University of New South Wales Sydney; Monash University; Hong Kong University of Science & Technology
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/1467-9868.00127
• 发表日期: 1998
• 页码: 311-331
• 关键词: VARIABLE SELECTION; MODEL

摘要

A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors. Each of the potentially non-linear components is modelled as a regression spline using many knots, while the errors are modelled by a high order stationary autoregressive process parameterized in terms of its autocorrelations. The distribution of significant knots and partial autocorrelations is accounted for using subset selection. Our approach also allows the selection of a suitable transformation of the dependent variable. All aspects of the model are estimated simultaneously by using the Markov chain Monte Carlo method. It is shown empirically that the approach proposed works well on several simulated and real examples.