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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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Ensemble Conditional Variance Estimator for Sufficient Dimension Reduction
Ensemble Conditional Variance Estimation (ECVE) is a novel sufficient di...
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Adaptive Function-on-Scalar Regression with a Smoothing Elastic Net
This paper presents a new methodology, called AFSSEN, to simultaneously ...
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Central Quantile Subspace
Quantile regression (QR) is becoming increasingly popular due to its rel...
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Ancestral Inference from Functional Data: Statistical Methods and Numerical Examples
Many biological characteristics of evolutionary interest are not scalar ...
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It's All Relative: New Regression Paradigm for Microbiome Compositional Data
Microbiome data are complex in nature, involving high dimensionality, co...
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Do Code Review Measures Explain the Incidence of Post-Release Defects?
Aim: In contrast to studies of defects found during code review, we aim ...
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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 regression model by using a prior which simultaneously selects, clusters, and smooths functional effects. Our methodology groups effects of highly correlated predictors, performing dimension reduction without dropping relevant predictors from the model. We validate our approach via a simulation study, showing superior performance relative to existing dimension reduction approaches in the function-on-scalar literature. We also demonstrate the use of our model on a data set of age specific fertility rates from the United Nations Gender Information database.
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