Use of multiplicative interaction explanatory variables has been a standard practice in the regression modeling literature, and estimation parameters such model case spatial autoregressive (SAR) or Durbin (SDM) models can be accomplished using existing software for estimation. However, use conventional scalar summary estimates direct indirect effects reflecting own- other-region impacts on depe...

Canonical correspondence analysis and redundancy analysis are two methods of constrained ordination regularly used in the analysis of ecological data when ordinations based on several response variables (for example, species abundances) are related linearly to several explanatory variables (for example, environmental variables, spatial positions of samples). In this report I demonstrate the adv...

This document provides supplementary material to our paper “Gender, Competitiveness and Career Choices”. Appendix I presents tables with additional results. This includes descriptive statistics of the control variables (A.I), a comparison of study tracks by gender in the national statistics and in our sample (A.II), the distribution of undergraduate major students by study track at the pre-univ...

In order to study the relationship between random Boolean sets and some explanatory variables, this paper introduces a Propagation model. This model can be applied when corresponding Poisson process of the Boolean model is related to explanatory variables and the random grains are not affected by these variables. An approximation for the likelihood is used to find pseudo-maximum likelihood esti...

one of the models that can be used to study the relationship between boolean random sets and explanatory variables is growth regression model which is defined by generalization of boolean model and permitting its grains distribution to be dependent on the values of explanatory variables. this model can be used in the study of behavior of boolean random sets when their coverage regions variation...