Variable Selection and Function Estimation in Additive Nonparametric Regression Using a Data-Based Prior
作者:
ThomasS. Shively,
Robert Kohn,
Sally Wood,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1999)
卷期:
Volume 94,
issue 447
页码: 777-794
ISSN:0162-1459
年代: 1999
DOI:10.1080/01621459.1999.10474180
出版商: Taylor & Francis Group
关键词: Bayesian information criterion;Binary regression;Gaussian errors;Model averaging;Posterior probabilities;Time series models
数据来源: Taylor
摘要:
A hierarchical Bayesian approach is proposed for variable selection and function estimation in additive nonparametric Gaussian regression models and additive nonparametric binary regression models. The prior for each component function is an integrated Wiener process resulting in a posterior mean estimate that is a cubic smoothing spline. Each of the explanatory variables is allowed to be in or out of the model, and the regression functions are estimated by model averaging. To allow variable selection and model averaging, data-based priors are used for the smoothing parameter and the slope at 0 of each component function. A two-step Markov chain Monte Carlo method is used to efficiently obtain the data-based prior and to carry out variable selection and function estimation. It is shown by simulation that significant improvements in the function estimators can be obtained over an approach that estimates all the unknown functions simultaneously. The methodology is illustrated for a binary regression using heart attack data.
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