Sparse modeling of categorial explanatory variables

Sparse Modeling of Categorial Explanatory Variables

Shrinking methods in regression analysis are usually designed for metric predictors. If independent variables are categorial some modifications are necessary. In this article two L1-penalty based methods for factor selection and clustering of categories are presented and investigated. The first approach is designed for nominal scale levels, the second one for ordinal predictors. All methods are...

Forward selection of explanatory variables.

This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. T...

Modelling with multiple explanatory variables.

Weighted-bootstrap Alignment of Explanatory Variables

Adjustment for covariates is a time-honored tool in statistical analysis and is often implemented by including the covariates that one intends to adjust as additional predictors in a model. This adjustment often does not work well when the underlying model is misspecified. We consider here the situation where we compare a response between two groups. This response may depend on a covariate for ...

Regression Transformation Diagnostics for Explanatory Variables

Two types of diagnostics are presented for the transformation of explanatory variables in regression. One is based on the likelihood displacement proposed by Cook and Weisberg (1982) for assessing the in uence of individual cases on the maximum likelihood estimate of a transformation parameter. The other is based on the local in uence theory proposed by Cook (1986) for assessing the in uence of...