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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 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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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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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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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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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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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, Xia, Yingcun
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