Loss-based estimation with evolutionary algorithms and cross-validation

David Shilane, Division of Biostatistics, School of Public Health, University of California, Berkeley
Richard H. Liang, Department of Statistics, University of California, Berkeley
Sandrine Dudoit, Division of Biostatistics and Department of Statistics, University of California, Berkeley

Abstract

Many statistical inference methods rely upon selection procedures to estimate a parameter of the joint distribution of explanatory and outcome data, such as the regression function. Within the general framework for loss-based estimation of Dudoit and van der Laan, this project proposes an evolutionary algorithm (EA) as a procedure for risk optimization. We also analyze the size of the parameter space for polynomial regression under an interaction constraints along with constraints on either the polynomial or variable degree.