Sufficient Dimension Reduction: An Information-Theoretic Viewpoint [PDF]
Entropy, 2022 There has been a lot of interest in sufficient dimension reduction (SDR) methodologies, as well as nonlinear extensions in the statistics literature. The SDR methodology has previously been motivated by several considerations: (a) finding data-driven ...
Debashis Ghosh
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A comparative evaluation of sufficient dimension reduction and traditional statistical methods for composite biomarker score construction in diagnostic classification [PDF]
BMC Medical Research MethodologyBackground Combining multiple biomarkers into a single diagnostic score can improve disease classification. However, traditional methods such as logistic regression and linear discriminant analysis depend on restrictive distributional assumptions, which ...
Hulya Ozen, Ertugrul Colak, Dogukan Ozen
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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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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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Cumulative Median Estimation for Sufficient Dimension Reduction
Stats, 2021 In this paper, we present the Cumulative Median Estimation (CUMed) algorithm for robust sufficient dimension reduction. Compared with non-robust competitors, this algorithm performs better when there are outliers present in the data and comparably when ...
Stephen Babos, Andreas Artemiou
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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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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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Dimension reduction with expectation of conditional difference measure
Statistical Theory and Related Fields, 2023 In this article, we introduce a flexible model-free approach to sufficient dimension reduction analysis using the expectation of conditional difference measure.
Wenhui Sheng, Qingcong Yuan
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An Adaptive-to-Model Test for Parametric Functional Single-Index Model
Mathematics, 2023 Model checking methods based on non-parametric estimation are widely used because of their tractable limiting null distributions and being sensitive to high-frequency oscillation alternative models.
Lili Xia, Tingyu Lai, Zhongzhan Zhang
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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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