Results 51 to 60 of about 846,250 (270)
PCA consistency in high dimension, low sample size context [PDF]
, 2009 Principal Component Analysis (PCA) is an important tool of dimension reduction especially when the dimension (or the number of variables) is very high. Asymptotic studies where the sample size is fixed, and the dimension grows [i.e., High Dimension, Low ...
Jung, Sungkyu, Marron, J. S.
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Quantile treatment effect estimation with dimension reduction
Statistical Theory and Related Fields, 2020 Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation.
Ying Zhang +3 more
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Energy Reports, 2021 One of the best options for managing energy markets and power system performance optimization is precise Electricity Price Forecasting (EPF). In detecting the prices of energy, some factors are of stochastic behavior which has made an unwieldy task.
Liangping Sun +3 more
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Estimating sufficient reductions of the predictors in abundant high-dimensional regressions [PDF]
, 2012 We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments.
Cook, R. Dennis +2 more
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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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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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Minimum Average Deviance Estimation for Sufficient Dimension Reduction
, 2016 Sufficient dimension reduction reduces the dimensionality of data while preserving relevant regression information. In this article, we develop Minimum Average Deviance Estimation (MADE) methodology for sufficient dimension reduction.
Adragni, Kofi P. +2 more
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Contour regression: A general approach to dimension reduction [PDF]
, 2005 We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of small variation in the response. These directions span the orthogonal complement of the minimal space relevant for the regression and ...
Chiaromonte, Francesca +2 more
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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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