Seminar at SMU Delhi
October 31, 2018 (Wednesday) ,
3:30 PM at Webinar
Bootstrap in Post Model Selection Inference
Abstract of Talk
Penalized regression techniques are widely used in practice to perform variable selection (also known as model selection). Variable selection is important to drop the covariates from the regression model which are irrelevant in explaining the response variable. When the number of covariates is large compared to the sample size, variable selection is indeed the most important requirement of the penalized method. Fan and Li (2001) introduced the Oracle Property as a measure of how good a penalized method is. A penalized method is said to have the oracle property provided it works as well as if the correct sub-model were known (like the Oracle who knows everything beforehand). We categorize different penalized regression methods with respect to oracle property and show that bootstrapworks for each category. Moreover we
show that in most of the situations, the inference based on bootstrap is much more accurate than the oracle based inference.