These includeSAS implements forward, backward, and stepwise selection in PROC REG with the SELECTION option on the MODEL statement. For a sample of size N, leave-one-out-cross-validation, or LOOCV, acts a little like a jackknife structure, taking N-1 of the data points to build the model and testing the results against the remaining single data point, in N systematic replicates, with the kth point being dropped in the kth replicate. I close by summarizing our results, making recommendations, and suggesting further readings.A variable selection method is a way of selecting a particular set of independent variables (IVs) for use in a regression model. There are no solutions to the problems that stepwise regression methods have. Stepwise regression: a bad idea!. How bad is stepwise regression? One additional problem is that the methods may not identify sets of variables that fit well, even when such sets exist Miller (2002).I detail why these methods are poor, and suggest some better alternatives. Then, these models are combined using:When one has too many variables, a standard data reduction technique is principal components analysis (PCA), and some have recommended PCA regression.

This involves reducing the number of IVs by using the largest eigenvalues of X’X. If they are not cautious, researchers using data mining techniques can be easily misled by these results.Let’s first build a data base for our experiment,  which consists of an dependent variable (DV) and 50 candidate variables (IP), the DB and IPs were generated independently (i.e no relation ship between DV and IVs).Let us build a stepwise regression on this data set through three methods, backward, forward and both. By searching many combination of candidate variable, this algorithm make the value of the statistics like p-value, AIC, BIC out of their propper meaning. Original post by DO Xuan Quang here. Conventional tests of statistical significance are based on the probability that an observation arose by chance, and necessarily accept some risk of mistaken test results, called the significance. Put in another way, for a data analyst to use stepwise methods is equivalent to telling his or her boss that his or her salary should be cut.

But sometimes there are problems. When people talk about using hold-out samples, this is not really cross-validation. Therefor it is suggested to use it only in exploratory research. Default criteria are p = 0.5 for forward selection, p = 0.1 for backward selection, and both of these for stepwise selection. I show how they can be implemented in SAS (PROC GLMSELECT) and offer pointers to how they can be done in R and Python.Stepwise methods are also problematic for other types of regression, but we do not discuss these. It … It yields p -values that do not have the proper meaning, and the proper correction for them is a difficult problem. You can quantify exactly how unlikely such an event is, given that the probability of heads on any one toss is 0.5. The wide range of options available in both these methods allows for considerable exploration, and for eliminating models that do not make substantive sense.For additional information on the problems posed by stepwise, Harrell (2001) offers a relatively nontechnical introduction, together with good advice on regression modeling in general. If you have 10 people each toss a coin ten times, and one of them gets 10 heads, you are less suspicious, but you can still quantify the likelihood. “Stepwise regression is one of these things, like outlier detection and pie charts, which appear to be popular among non-statisticans but are considered by statisticians to be a bit of a joke.” Tibshirani and Hastie in their recent Statistical Learning MOOC were quite positive about stepwise regression, in particular forward stepwise selection for variable selection.