Results 21 to 30 of about 103,329 (151)
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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Fusing sufficient dimension reduction with neural networks
Computational Statistics & Data Analysis, 2022 19 pages, 4 figures, 10 ...
Daniel Kapla +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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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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Forecasting with Sufficient Dimension Reductions [PDF]
Finance and Economics Discussion Series, 2015 Factor models have been successfully employed in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. When the objective is to forecast a target variable y with a large set of predictors x, the construction of the summary of the xs should be driven by how informative on y ...
Alessandro Barbarino, Efstathia Bura
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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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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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Dimension estimation in sufficient dimension reduction: A unifying approach
Journal of Multivariate Analysis, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Efstathia Bura, Jiao Yang
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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
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