Robust Estimation with Discrete Explanatory Variables

Modelling with multiple explanatory variables.

Integer Valued AR Processes with Explanatory Variables

Integer valued AR (INAR) processes are perfectly suited for modelling count data. We consider the inclusion of explanatory variables into the INAR model to extend the applicability of INAR models and give an alternative to Poisson regression models. An efficient MCMC algorithm is constructed to analyze the model and incorporates both explanatory variable and order selection. The methodology is ...

Linear regression with compositional explanatory variables

Compositional explanatory variables should not be directly used in a linear regression model because any inference statistic can become misleading. While various approaches for this problem were proposed, here an approach based on the isometric logratio (ilr) transformation is used. It turns out that the resulting model is easy to handle, and that parameter estimation can be done like in usual ...

Instrumental Variables Estimation of Nonparametric Models with Discrete Endogenous Regressors

This paper presents new instrumental variables estimators for nonparametric models with discrete endogenous regressors. The model speci cation is su ciently general to include structural models, triangular simultaneous equations and certain models of measurement error. Restricting the analysis to discrete endogenous regressors is an integral component of the analysis since a similar model with ...

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...