Adaptive Estimation with Partially Overlapping Models.

TitleAdaptive Estimation with Partially Overlapping Models.
Publication TypeJournal Article
Year of Publication2016
AuthorsShin, Sunyoung, Jason Fine, and Yufeng Liu
JournalStat Sin
Date Published2016 Jan

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 estimation in the regression setting. The recovery of the overlap structure enables more efficient parameter estimation. An oracle result is established. Simulation studies illustrate the advantages of ACME over existing methods that fit individual models separately or make strong a priori assumption about the overlap structure.

Alternate JournalStat Sin
Original PublicationAdaptive estimation with partially overlapping models.
PubMed ID26917931
PubMed Central IDPMC4762277
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA149569 / CA / NCI NIH HHS / United States