On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models

We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting. Such models form a rich class of non-linear models with applications to signal processing, machine learning and statistics. Our estimators leverage the score function based first and second-order Stein's identities and do not require the covariates to satisfy Gaussian or elliptical symmetry assumptions common in the literature. Moreover, to handle score functions and responses that are heavy-tailed, our estimators are constructed via carefully thresholding their empirical counterparts. We show that our estimator achieves near-optimal statistical rate of convergence in several settings. We supplement our theoretical results via simulation experiments that confirm the theory

SPAD values of Star and Suziblue at different time and locations.

SPAD values of Star and Suziblue at different time and locations.</p

Additional file 1: Table S1. of Identification of somatic mutations using whole-exome sequencing in Korean patients with acute myeloid leukemia

Details of the 36 AML patients. Table S2. Functional information for 15 significantly mutated genes in 36 Korean AML patients. Table S3. Results of gene ontology and KEGG pathway analyses. (DOCX 38 kb