Variable selection in robust regression models for longitudinal data. Journal of Multivariate Analysis 109, 156-167.Fan, Y., Qin. G., Zhu, Z. (2012). Variable selection in robust regression models for longitudinal data. J. Multivariate Anal. 109:156-167....
An objective Bayesian procedure for variable selection in regression - Girón, Moreno, et al. - 2006Giro´n, FJ, Moreno, E, Mart´inez, ML. An Objective Bayesian Procedure for Variable Selection in Regression. Birkh¨auser Boston, 2006b....
Forward selection Main idea: add one variable at each time. Steps: Start with no covariate in the model Add the most significant covariate: ①Fit p simple linear regression models y=β0+β1xj+ϵ , for j=1,...,p . For each model, calculate the p -value using single t -test for...
We propose a method for variable selection in multiple regression with random predictors. This method is based on a criterion that permits to reduce the variable selection problem to a problem of estimating suitable permutation and dimensionality. Then, estimators for these parameters are proposed and...
a polynomial regression model that excludes hierarchically inferior predictors (i.e., lower-order terms) is considered to be not well formulated. Existing variable-selection algorithms do not take into account the hierarchy of predictors and often select as "best" a model that is not hierarchically...
Febrero-Bande, M., Gonz´alez-Manteiga, W., & de la Fuente, M. O. (2017). Variable selection in functional additive regression models. In G. Aneiros, E. G. Bongiorno, R. Cao, & P. Vieu (Eds.), Functional Statistics and Related Fields (pp. 113-122). Cham: Springer Interna- ...
Variable Selection for Logistic Regression Using a Prediction Focussed Information Criterion In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the Focussed Information Cr... G Claeskens,C Croux,J Van Kerckhoven - 《...
14 Variable Selection for Regression Analysis 14.1 Regression Analysis In the preceding chapter, we described the application of branch-and-bound methods for the selection of variables for cluster analysis and pat-tern recognition. There are, however, other important variable selection applications and ...
In deriving a regression model analysts often have to use variable selection, despite of problems introduced by data- dependent model building. Resampling approaches are proposed to handle some of the critical issues. In order to assess and compare several strategies, we will conduct a simulation st...
Enter (Regression). A procedure for variable selection in which all variables in a block are entered in a single step. Stepwise. At each step, the independent variable not in the equation that has the smallest probability of F is entered, if that probability is sufficiently small. Variables ...