Results 11 to 20 of about 103,963 (248)
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.
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A selective overview of sparse sufficient dimension reduction
Statistical Theory and Related Fields, 2020 High-dimensional data analysis has been a challenging issue in statistics. Sufficient dimension reduction aims to reduce the dimension of the predictors by replacing the original predictors with a minimal set of their linear combinations without loss of ...
Lu Li, Xuerong Meggie Wen, Zhou Yu
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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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Sparse sufficient dimension reduction for directional regression
Journal of Big DataSufficient 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
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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
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Sufficient Dimension Reduction via Bayesian Mixture Modeling [PDF]
Biometrics, 2011 Howard Bondell, Brian Reich
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Sufficient dimension reduction and prediction in regression [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2009 Dimension reduction for regression is a prominent issue today because technological advances now allow scientists to routinely formulate regressions in which the number of predictors is considerably larger than in the past. While several methods have been proposed to deal with such regressions, principal components (PCs) still seem to be the most ...
Adragni, Kofi P., Cook, R. Dennis
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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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Fréchet sufficient dimension reduction for random objects [PDF]
Biometrika, 2022 Summary We consider Fréchet sufficient dimension reduction with responses being complex random objects in a metric space and high-dimensional Euclidean predictors. We propose a novel approach, called the weighted inverse regression ensemble method, for linear Fréchet sufficient dimension reduction.
Ying, Chao, Yu, Zhou
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direpack: A Python 3 package for state-of-the-art statistical dimensionality reduction methods
SoftwareX, 2023 The direpack package establishes a set of modern statistical dimensionality reduction techniques into the Python universe as a single, consistent package.
Emmanuel Jordy Menvouta +2 more
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