Results 11 to 20 of about 5,211,856 (273)

Sufficient dimension reduction on partially nonlinear index models with applications to medical costs analysis. [PDF]

PLoS ONE
Modeling medical costs is a crucial task in health economics, especially when high-dimensional covariates and nonlinear effects are present. In this study, we propose a partially nonlinear index model (PNIM) that integrates partially sufficient dimension
Xiaobing Zhao, Yufeng Xia, Xuan Xu
doaj   +2 more sources

Projection expectile regression for sufficient dimension reduction. [PDF]

Comput Stat Data Anal, 2023
Many existing sufficient dimension reduction methods are designed for regression with predictors that are elliptically distributed, which limits their application in real data analyses. Projection expectile regression (PER) is proposed as a new linear sufficient dimension reduction method for handling complex predictor structures, which includes ...
Soale AN.
europepmc   +3 more sources

Fusing sufficient dimension reduction with neural networks

Computational Statistics & Data Analysis, 2022
19 pages, 4 figures, 10 ...
Daniel Kapla, Lukas Fertl, Efstathia Bura   +2 more
openaire   +4 more sources

Sufficient dimension reduction for feature matrices

CoRR, 2023
We address the problem of sufficient dimension reduction for feature matrices, which arises often in sensor network localization, brain neuroimaging, and electroencephalography analysis. In general, feature matrices have both row- and column-wise interpretations and contain structural information that can be lost with naive vectorization approaches. To
Lee, Chanwoo
openaire   +3 more sources

Sufficient Dimension Reduction via Distance Covariance [PDF]

Journal of Computational and Graphical Statistics, 2016
We introduce a novel approach to sufficient dimension reduction problems using distance covariance. Our method requires very mild conditions on the predictors. It estimates the central subspace effectively even when many predictors are categorical or discrete. Our method keeps the model-free advantage without estimating link function.
Xiangrong Yin, Wenhui Sheng
exaly   +2 more sources

Sufficient dimension reduction for longitudinally measured predictors. [PDF]

Stat Med, 2012
We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker ...
Pfeiffer RM, Forzani L, Bura E.
europepmc   +5 more sources

Sparse sufficient dimension reduction for directional regression

Journal of Big Data
Sufficient dimension reduction has emerged as a powerful tool for extracting meaningful information within high dimensional datasets over the past few decades.
Gayun Kwon, Gijeong Noh, Kyongwon Kim
doaj   +3 more sources

Conditional variance estimator for sufficient dimension reduction

Bernoulli, 2022
23 pages, 3 ...
Fertl, Lukas, Bura, Efstathia
openaire   +3 more sources

Some statistical methods for dimension reduction [PDF]

, 2013
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe aim of the work in this thesis is to carry out dimension reduction (DR) for high dimensional (HD) data by using statistical methods for variable ...
Al-Kenani, Ali J Kadhim
core   +6 more sources

Sufficient dimension reduction via bayesian mixture modeling. [PDF]

Biometrics, 2011
Reich BJ, Bondell HD, Li L.
europepmc   +2 more sources