Results 21 to 30 of about 5,211,856 (273)
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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Sufficient dimension reduction with additional information [PDF]
Biostatistics, 2015 Sufficient dimension reduction is widely applied to help model building between the response $Y$ and covariate $X$. While the target of interest is the relationship between $(Y,X)$, in some applications we also collect additional variable $W$ that is strongly correlated with $Y$.
Hung, Hung +2 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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Sufficient dimension reduction for populations with structured heterogeneity
Biometrics, 2021 AbstractA key challenge in building effective regression models for large and diverse populations is accounting for patient heterogeneity. An example of such heterogeneity is in health system risk modeling efforts where different combinations of comorbidities fundamentally alter the relationship between covariates and health outcomes.
Jared D. Huling, Menggang Yu
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AN ADAPTIVE COMPOSITE QUANTILE APPROACH TO DIMENSION REDUCTION [PDF]
, 2014 Sufficient dimension reduction [Li 1991] has long been a prominent issue in multivariate nonparametric regression analysis. To uncover the central dimension reduction space, we propose in this paper an adaptive composite quantile approach.
Kong, Efang
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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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Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms
Frontiers in Neuroscience, 2022 The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased ...
Debashis Ghosh +3 more
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