Abstract #300158

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JSM 2003 Abstract #300158
Activity Number: 447
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #300158
Title: Prediction with Simple Linear Model: Is it Really That Simple?
Author(s): Yuhong Yang*+
Companies: Iowa State University
Address: Snedecor Hall, Ames, IA, 50011-1210,
Keywords: model selection ; prediction ; model combining ; linear regression
Abstract:

Consider the problem of prediction with a simple linear model assumed on the relationship between a response variable Y and a univariate predictor X. Suppose it is not quite clear if the slope of the regression line, say beta, is zero or not. For such a case, usually, one performs a simple t-test and the prediction at a new X value is then made according to the outcome of the test. Alternatively to the t-test, one can use a model selection criterion such as AIC or BIC to judge if beta equals 0. The penalty of AIC is not large enough for consistency in model selection. For the prediction problem, however, the penalty may well be too large judged by the mean square prediction error. We propose alternative prediction methods and compare the performance of all the methods mentioned above. Advantages of the new methods are demonstrated.

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