Comparación de procedimientos FDR para la selección de parámetros en Regresión Poisson

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The selection of significant variables in regression models is an important problem in applied statistics. Poisson Regression, useful when it is of interest to describe the number of occurrences of a particular event as a function of exploratory variables, has recently been used for modeling purposes in biology, epidemiology, genetics and engineering. Here, the Poisson Regression model as well as four procedures to select variables, all of them based on the False Discovery Rate (FDR), are described. In addition, these procedures are compared using a simulation study and some recommendations are given. As reference, the t-based and Bonferroni procedures were used. Finally, we model the number teenagers with children in the Department of Antioquia to illustrate these methods.