4 research outputs found
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 38Â kb
Additional file 5: Table S4. of Comparative analysis of gut microbiota associated with body mass index in a large Korean cohort
Comparison of regression analysis with or without adjustment of T2DM or T2DM under medication as covariates. (DOCX 19Â kb