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An Optimal Prediction Function for the Normal Linear Model

 

作者: MartinS. Levy,   S.K. Perng,  

 

期刊: Journal of the American Statistical Association  （Taylor Available online 1986）
卷期: Volume 81, issue 393  

页码: 196-198

 

ISSN:0162-1459

 

年代: 1986

 

DOI:10.1080/01621459.1986.10478259

 

出版商: Taylor & Francis Group

 

关键词: Prediction density;Linear models;Predictive inference;Kullback—Leibler divergence

 

数据来源: Taylor

 

摘要:

A prediction density functiong* for the normal linear model is derived. This function is shown to dominate three well-known prediction densities by first constructing a specified class of densities that includes these three and then proving thatg* is the optimal member of this class in the sense of minimizing a criterion based on the Kullback—Leibler divergence.g* coincides with a Bayesian prediction density assuming diffuse prior.

 

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