April 24, 2012
In a new article in the Journal of the American Statistical Society, authors Lan Wang, Yichao Wu, and Runze Li describe a new approach to accommodating heterogeneity in ultrahigh-dimensional data. The authors advocate a more general interpretation of sparsity, which assumes that only a small number of covariates influence the conditional distribution of the response variable, given all candidate covariates. Note that the sets of relevant covariates may differ when we consider different segments of the conditional distribution.
Wang, L., Wu, Y., & Li, R. (2012) Quantile regression for analyzing heterogeneity in ultra-high dimension. Journal of the American Statistical Association, 107(497).