Reconciling Model Selection and Prediction Export

@article{citeulike:4214090, abstract = {It is known that there is a dichotomy in the performance of model selectors. Those that are consistent (having the "oracle property") do not achieve the asymptotic minimax rate for prediction error. We look at this phenomenon closely, and argue that the set of parameters on which this dichotomy occurs is extreme, even pathological, and should not be considered when evaluating model selectors. We characterize this set, and show that, when such parameters are dismissed from consideration, consistency and asymptotic minimaxity can be attained simultaneously.}, archivePrefix = {arXiv}, author = {Casella, George and Consonni, Guido}, citeulike-article-id = {4214090}, citeulike-linkout-0 = {http://arxiv.org/abs/0903.3620}, citeulike-linkout-1 = {http://arxiv.org/pdf/0903.3620}, day = {20}, eprint = {0903.3620}, keywords = {consistency, model-selection, optimal, prediction, statistics}, month = {Mar}, posted-at = {2009-10-23 18:26:41}, priority = {2}, title = {Reconciling Model Selection and Prediction}, url = {http://arxiv.org/abs/0903.3620}, year = {2009} }

TY - JOUR ID - citeulike:4214090 L3 - citeulike-article-id:4214090 N2 - It is known that there is a dichotomy in the performance of model selectors. Those that are consistent (having the "oracle property") do not achieve the asymptotic minimax rate for prediction error. We look at this phenomenon closely, and argue that the set of parameters on which this dichotomy occurs is extreme, even pathological, and should not be considered when evaluating model selectors. We characterize this set, and show that, when such parameters are dismissed from consideration, consistency and asymptotic minimaxity can be attained simultaneously. TI - Reconciling Model Selection and Prediction KW - consistency KW - model-selection KW - optimal KW - prediction KW - statistics AU - Casella, George AU - Consonni, Guido PY - 2009/Mar/20/ UR - http://arxiv.org/abs/0903.3620 ER -