Statistical Dependence of Pipe Breaks on Explanatory Variables

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.

An Index for Selecting Mixed Explanatory Variables in the Analysis of Dependence

In the analysis of multivariate dependence, two main objectives may be pursued: descriptive and decisional. In the descriptive approach, the selection of explanatory variables is determined by the researcher’s objectives and form the nature of the problem at hand. The decisional approach requires a parsimonious model where only the variables with the highest explanatory power are included. This...

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...

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 ...