Results 21 to 30 of about 845,542 (252)
Gene set analysis using sufficient dimension reduction. [PDF]
BMC Bioinformatics, 2016 Abstract Background Gene set analysis (GSA) aims to evaluate the association between the expression of biological pathways, or a priori defined gene sets, and a particular phenotype. Numerous GSA methods have been proposed to assess the enrichment of sets of genes.
Hsueh HM, Tsai CA.
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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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Model averaging‐based sufficient dimension reduction
Stat, 2022 Sufficient dimension reduction is intended to project high‐dimensional predictors onto a low‐dimensional space without loss of information on the responses. Classical methods, such as sliced inverse regression, sliced average variance estimation and directional regression, are backbones of many modern sufficient dimension methods and have gained ...
Min Cai, Ruige Zhuang, Zhou Yu, Ping Wu
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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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Probability-enhanced sufficient dimension reduction for binary classification. [PDF]
Biometrics, 2014 SummaryIn high‐dimensional data analysis, it is of primary interest to reduce the data dimensionality without loss of information. Sufficient dimension reduction (SDR) arises in this context, and many successful SDR methods have been developed since the introduction of sliced inverse regression (SIR) [Li (1991)Journal of the American Statistical ...
Shin SJ, Wu Y, Zhang HH, Liu Y.
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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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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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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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