Results 21 to 30 of about 846,250 (270)
Sufficient dimension reduction via bayesian mixture modeling. [PDF]
Biometrics, 2011 Dimension reduction is central to an analysis of data with many predictors. Sufficient dimension reduction aims to identify the smallest possible number of linear combinations of the predictors, called the sufficient predictors, that retain all of the information in the predictors about the response distribution.
Reich BJ, Bondell HD, Li L.
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Sufficient dimension reduction on partially nonlinear index models with applications to medical costs analysis. [PDF]
PLoS ONEModeling 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
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Testing predictor contributions in sufficient dimension reduction
The Annals of Statistics, 2004 We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the conditional distribution of the response given the predictors. Predictor effects need not be limited to the mean function and smoothing is not required.
Cook, R. Dennis
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LDR: A Package for Likelihood-Based Sufficient Dimension Reduction [PDF]
Journal of Statistical Software, 2011 We introduce a new mlab software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation ...
R. Dennis Cook +2 more
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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.
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Gene set analysis using sufficient dimension reduction. [PDF]
BMC Bioinformatics, 2016 Abstract Background Gene set analysis (GSA) aims to evaluate the association between the expression of biological pathways, or a priori defined gene sets, and a particular phenotype. Numerous GSA methods have been proposed to assess the enrichment of sets of genes.
Hsueh HM, Tsai CA.
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Probability-enhanced sufficient dimension reduction for binary classification. [PDF]
Biometrics, 2014 SummaryIn high‐dimensional data analysis, it is of primary interest to reduce the data dimensionality without loss of information. Sufficient dimension reduction (SDR) arises in this context, and many successful SDR methods have been developed since the introduction of sliced inverse regression (SIR) [Li (1991)Journal of the American Statistical ...
Shin SJ, Wu Y, Zhang HH, Liu Y.
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Transformed sufficient dimension reduction [PDF]
Biometrika, 2014 A novel general framework is proposed in this paper for dimension reduction in regression to fill the gap between linear and fully nonlinear dimension reduction. The main idea is to transform first each of the raw predictors monotonically, and then search for a low-dimensional projection in the space defined by the transformed variables.
T. Wang, X. Guo, L. Zhu, P. Xu
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Model averaging‐based sufficient dimension reduction
Stat, 2022 Sufficient dimension reduction is intended to project high‐dimensional predictors onto a low‐dimensional space without loss of information on the responses. Classical methods, such as sliced inverse regression, sliced average variance estimation and directional regression, are backbones of many modern sufficient dimension methods and have gained ...
Min Cai, Ruige Zhuang, Zhou Yu, Ping Wu
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Aggregate Kernel Inverse Regression Estimation
Mathematics, 2023 Sufficient dimension reduction (SDR) is a useful tool for nonparametric regression with high-dimensional predictors. Many existing SDR methods rely on some assumptions about the distribution of predictors. Wang et al.
Wenjuan Li +3 more
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