Adaptive Estimation with Partially Overlapping Models.

ABSTRACT: In many problems, one has several models of interest that capture key parameters describing the distribution of the data. Partially overlapping models are taken as models in which at least one covariate effect is common to the models. A priori knowledge of such structure enables efficient estimation of all model parameters. However, in practice, this structure may be unknown. We propose adaptive composite M-estimation (ACME) for partially overlapping models using a composite loss function, which is a linear combination of loss functions defining the individual models. Penalization is applied to pairwise differences of parameters across models, resulting in data driven identification of the overlap structure. Further penalization is imposed on the individual parameters, enabling sparse esti

SUBMITTER: Shin S 

PROVIDER: S-EPMC4762277 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature