کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1150618 | 957960 | 2007 | 15 صفحه PDF | دانلود رایگان |
In this paper, we consider the prediction problem in multiple linear regression model in which the number of predictor variables, p, is extremely large compared to the number of available observations, n . The least-squares predictor based on a generalized inverse is not efficient. We propose six empirical Bayes estimators of the regression parameters. Three of them are shown to have uniformly lower prediction error than the least-squares predictors when the vector of regressor variables are assumed to be random with mean vector zero and the covariance matrix (1/n)XtX(1/n)XtX where Xt=(x1,…,xn)Xt=(x1,…,xn) is the p×np×n matrix of observations on the regressor vector centered from their sample means. For other estimators, we use simulation to show its superiority over the least-squares predictor.
Journal: Journal of Statistical Planning and Inference - Volume 137, Issue 11, 1 November 2007, Pages 3778–3792