Hello, all, I am using Stepwise multiple linear regression. The model has 5 independent variables, and number of sample is 210. I found the following suggestion today.
************************************************************************ Stepwise regression is used in the exploratory phase of research or for purposes of pure prediction,, not theory testing. In the theory testing stage the researcher should base selection of the variables and their order on theory, not on a computer algorithm. Menard (1995: 54) writes, "there appears to be general agreement that the use of computer-controlled stepwise procedures to select variables is inappropriate for theory testing because it capitalizes on random variations in the data and produces results that tend to be idosyncratic and difficult to replicate in any sample other than the sample in which they were originally obtained." Likewise, the nominal .05 significance level used at each step in stepwise regression is subject to inflation, such that the real significance level by the last step may be much worse, even below .50, dramatically increasing the chances of Type I errors. See Draper, N.R., Guttman, I. & Lapczak, L. (1979). For this reason, Fox (1991: 18) strongly recommends any stepwise model be subjected to cross-validation. ************************************************************************* I know cross-validation is useful for neural network. I am not sure I have to use it in MLR, because my linear model need "overfitting" for all samples. I donot like cross validation, maybe the main reason is that I have not software do it. So I wonder if you can give me a excuse so that I do not need cross validation. In practice, is it popular method that stepwise model is subjected to cross-validation? thank you very much. Bin . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
