Prof. Yanyuan Ma (University of South Carolina) Sufficient Direction Factor Model

We study the sufficient dimension reduction problem in the ultra high dimensional covariate setting.
Through introducing latent factors, we avoid the usual sparsity assumption and do not resort to
penalization for screening of the covariates. Our treatment does not make the normality assumption
on the latent factor distributions. We derive the asymptotic distribution theory of the method. The
procedure can be further generalized to sufficient direction analysis in generalized linear latent
variable models.