A0380
Title: Asymptotic theory of sparse factor models in high-dimension
Authors: Benjamin Poignard - Osaka University (Japan) [presenting]
Yoshikazu Terada - Osaka University; RIKEN (Japan)
Abstract: The problem of estimating a factor model-based variance-covariance matrix is considered when the factor loading matrix is assumed sparse. We develop a penalized estimating function framework to handle the identifiability issue of the factor loading matrix while fostering sparsity in potentially all its entries. We prove the oracle property of the penalized estimator for the factor model, that is the penalization procedure can recover the true sparse support and the estimator is asymptotically normally distributed. Consistency and support recovery are established when the number of parameters is diverging. The non-penalized loss functions are deduced from the class of Bregman divergence losses, providing new estimators for factor modelling. These theoretical results are supported by empirical studies.
