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[BOK][B] Sufficient dimension reduction: Methods and applications with R
B Li - 2018 - taylorfrancis.com
Sufficient dimension reduction is a rapidly develo** research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …
applications in regression diagnostics, data visualization, machine learning, genomics …
Dimension reduction for high-dimensional data
L Li - Statistical methods in molecular biology, 2010 - Springer
With advancing of modern technologies, high-dimensional data have prevailed in
computational biology. The number of variables p is very large, and in many applications, p …
computational biology. The number of variables p is very large, and in many applications, p …
Kernel Partial Correlation Coefficient---a Measure of Conditional Dependence
We propose and study a class of simple, nonparametric, yet interpretable measures of
conditional dependence, which we call kernel partial correlation (KPC) coefficient, between …
conditional dependence, which we call kernel partial correlation (KPC) coefficient, between …
[HTML][HTML] High dimensional single index models
P Radchenko - Journal of Multivariate Analysis, 2015 - Elsevier
This paper addresses the problem of fitting nonlinear regression models in high-
dimensional situations, where the number of predictors, p, is large relative to the number of …
dimensional situations, where the number of predictors, p, is large relative to the number of …
Sparse SIR: Optimal rates and adaptive estimation
Sparse SIR: Optimal rates and adaptive estimation Page 1 The Annals of Statistics 2020, Vol.
48, No. 1, 64–85 https://doi.org/10.1214/18-AOS1791 © Institute of Mathematical Statistics …
48, No. 1, 64–85 https://doi.org/10.1214/18-AOS1791 © Institute of Mathematical Statistics …
A concise overview of principal support vector machines and its generalization
In high-dimensional data analysis, sufficient dimension reduction (SDR) has been
considered as an attractive tool for reducing the dimensionality of predictors while …
considered as an attractive tool for reducing the dimensionality of predictors while …
On post dimension reduction statistical inference
On post dimension reduction statistical inference Page 1 The Annals of Statistics 2020, Vol. 48,
No. 3, 1567–1592 https://doi.org/10.1214/19-AOS1859 © Institute of Mathematical Statistics …
No. 3, 1567–1592 https://doi.org/10.1214/19-AOS1859 © Institute of Mathematical Statistics …
On marginal sliced inverse regression for ultrahigh dimensional model-free feature selection
Abstract Model-free variable selection has been implemented under the sufficient dimension
reduction framework since the seminal paper of Cook Ann. Statist. 32 (2004) 1062–1092. In …
reduction framework since the seminal paper of Cook Ann. Statist. 32 (2004) 1062–1092. In …
Asymptotic properties of sufficient dimension reduction with a diverging number of predictors
Y Wu, L Li - Statistica Sinica, 2011 - pmc.ncbi.nlm.nih.gov
We investigate asymptotic properties of a family of sufficient dimension reduction estimators
when the number of predictors p diverges to infinity with the sample size. We adopt a …
when the number of predictors p diverges to infinity with the sample size. We adopt a …
[HTML][HTML] Non-convex penalized estimation in high-dimensional models with single-index structure
T Wang, PR Xu, LX Zhu - Journal of Multivariate Analysis, 2012 - Elsevier
As promising alternatives to the LASSO, non-convex penalized methods, such as the SCAD
and the minimax concave penalty method, produce asymptotically unbiased shrinkage …
and the minimax concave penalty method, produce asymptotically unbiased shrinkage …