IDENTIFYING DRIVING FACTORS BEHIND INDIAN MONSOON PRECIPITATION USING MODEL SELECTION BASED ON DATA DEPTH

Abstract

We introduce a novel one-step model selection technique for general regression estimators, and implement it in a linear mixed model setup to identify important predictors affecting Indian Monsoon precipitation. Under very general assumptions, this technique correctly identifies the set of non-zero values in the true coefficient (of length p) by comparing only p + 1 models. Here we use wild bootstrap to estimate the selection criterion. Mixed models built on predictors selected by our procedure are more stable and accurate than full models across testing years in predicting median daily rainfall at a station.