Results 41 to 50 of about 5,211,856 (273)
Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction
Journal of Causal Inference, 2019 Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional
Luo Wei, Wu Wenbo, Zhu Yeying
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kag85/RHEED-Dimension-Reduction: Journal of Applied Physics
, 2021 An explanation of how to use dimension reduction methods PCA, NMF, and kmeans, on RHEED ...
kag85
core +1 more source
ManifoldOptim: An R Interface to the ROPTLIB Library for Riemannian Manifold Optimization
Journal of Statistical Software, 2020 Manifold optimization appears in a wide variety of computational problems in the applied sciences. In recent statistical methodologies such as sufficient dimension reduction and regression envelopes, estimation relies on the optimization of likelihood ...
Sean Martin +3 more
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Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing
Sensors, 2022 Fault diagnosis (FD) plays a vital role in building a smart factory regarding system reliability improvement and cost reduction. Recent deep learning-based methods have been applied for FD and have obtained excellent performance.
Xiaorui Shao, Chang-Soo Kim
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Impact of sufficient dimension reduction in nonparametric estimation of causal effect
Statistical Theory and Related Fields, 2018 We consider the estimation of causal treatment effect using nonparametric regression or inverse propensity weighting together with sufficient dimension reduction for searching low-dimensional covariate subsets.
Ying Zhang +3 more
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Optimization of Multidimensional Energy Security: An Index Based Assessment
Energies, 2022 This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization.
Fahad Bin Abdullah +4 more
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Multiple phenotype association tests based on sliced inverse regression
BMC BioinformaticsBackground Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in ...
Wenyuan Sun +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Numerous international studies have reported declines in new cancer diagnoses, delayed diagnoses and disruptions in cancer treatment following the implementation of COVID‐19 pandemic public health measures, raising concerns that these effects may ultimately contribute to increased cancer mortality.
Friederike Erdmann +8 more
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Sufficient Dimension Reduction for Interactions
, 2020 Dimension reduction lies at the heart of many statistical methods. In regression, dimension reduction has been linked to the notion of sufficiency whereby the relation of the response to a set of predictors is explained by a lower dimensional subspace in the predictor space.
Park, Hyung +3 more
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Sufficient dimension reduction for visual sequence classification [PDF]
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010 When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensionality reduction can be employed to find a low-dimensional representation on which classification can be done more efficiently.
Alex Shyr +2 more
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