A0299
Title: Sparse factor models of high dimension
Authors: Benjamin Poignard - Osaka University (Japan) [presenting]
Yoshikazu Terada - Osaka University; RIKEN (Japan)
Abstract: The estimation of the factor model-based variance-covariance matrix is considered when the factor loading matrix is assumed sparse. The estimation problem is recast as a penalized M-estimation criterion where the identification issue of the factor loading matrix is accounted for while fostering sparsity in potentially all its entries. Consistency and recovery of the true zero entries are established when the number of parameters is diverging. These theoretical results are supported by simulation experiments, and the relevance of the proposed method is illustrated by real data applications.
