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Sufficient Dimension Reduction for Interactions
Dimension reduction lies at the heart of many statistical methods. In re...
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Sufficient dimension reduction for classification using principal optimal transport direction
Sufficient dimension reduction is used pervasively as a supervised dimen...
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Dimension reduction for model-based clustering
We introduce a dimension reduction method for visualizing the clustering...
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Dimension reduction as an optimization problem over a set of generalized functions
Classical dimension reduction problem can be loosely formulated as a pro...
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Supervised Kernel PCA For Longitudinal Data
In statistical learning, high covariate dimensionality poses challenges ...
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Simultaneous Variable Selection, Clustering, and Smoothing in Function on Scalar Regression
We address the problem of multicollinearity in a function-on-scalar regr...
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Constraint matrix factorization for space variant PSFs field restoration
Context: in large-scale spatial surveys, the Point Spread Function (PSF)...
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Dynamic Partial Sufficient Dimension Reduction
Sufficient dimension reduction aims for reduction of dimensionality of a regression without loss of information by replacing the original predictor with its lower-dimensional subspace. Partial (sufficient) dimension reduction arises when the predictors naturally fall into two sets, X and W, and we seek dimension reduction on X alone while considering all predictors in the regression analysis. Though partial dimension reduction is a very general problem, only very few research results are available when W is continuous. To the best of our knowledge, these methods generally perform poorly when X and W are related, furthermore, none can deal with the situation where the reduced lower-dimensional subspace of X varies dynamically with W. In this paper, We develop a novel dynamic partial dimension reduction method, which could handle the dynamic dimension reduction issue and also allows the dependency of X on W. The asymptotic consistency of our method is investigated. Extensive numerical studies and real data analysis show that our Dynamic Partial Dimension Reduction method has superior performance comparing to the existing methods.
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