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Cross-validating regression models in marketing research

Author: Steckel, Joel H. ; Vanhonacker, Wilfried R.INSEAD Area: Marketing Series: Working Paper ; 90/42/MKT Publisher: Fontainebleau : INSEAD, 1990.Language: EnglishDescription: 32 p.Type of document: INSEAD Working Paper Online Access: Click here Abstract: In this paper, a formal test is developed for the cross-validation of regression models under the simple random splitting framework. Analytical as well as simulation results relate the statistical power of the test to the allocation of sample observations to the estimation and validation samples. The results indicate that splitting the data into halves is suboptimal. More observations should be used for estimation than validation. Furthermore, the proportion of the sample optimally devoted to validation decreases as the sample size increases. However, although the 50/50 split is suboptimal, it is not tremendously so in a wide variety of circumstances
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In this paper, a formal test is developed for the cross-validation of regression models under the simple random splitting framework. Analytical as well as simulation results relate the statistical power of the test to the allocation of sample observations to the estimation and validation samples. The results indicate that splitting the data into halves is suboptimal. More observations should be used for estimation than validation. Furthermore, the proportion of the sample optimally devoted to validation decreases as the sample size increases. However, although the 50/50 split is suboptimal, it is not tremendously so in a wide variety of circumstances

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