Results 41 to 50 of about 845,542 (252)
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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MULTI-FREQUENCY POLINSAR DATA ARE ADVANTAGEOUS FOR LAND COVER CLASSIFICATION – A VISUAL AND QUANTITATIVE ANALYSIS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 This paper investigates the enhanced potential of using multi-frequency PolInSAR data for land cover classification. In order to enable a descriptive analysis that goes beyond the mere comparison of classification accuracies, a two-step classification ...
S. Schmitz +4 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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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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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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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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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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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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Sufficient dimension reduction based on an ensemble of minimum average variance estimators
, 2012 We introduce a class of dimension reduction estimators based on an ensemble of the minimum average variance estimates of functions that characterize the central subspace, such as the characteristic functions, the Box--Cox transformations and wavelet ...
Li, Bing, Yin, Xiangrong
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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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