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Experimental design
TLDR
Maximizing data information requires careful selection, termed design, of the points at which data are observed. Expand
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Sparse linear discriminant analysis by thresholding for high dimensional data
In many social, economical, biological and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because theExpand
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Estimation in high-dimensional linear models with deterministic design matrices
Because of the advance in technologies, modern statistical studies often encounter linear models with the number of explanatory variables much larger than the sample size. Estimation and variableExpand
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Penalized Covariance Matrix Estimation Using a Matrix-Logarithm Transformation
For statistical inferences that involve covariance matrices, it is desirable to obtain an accurate covariance matrix estimate with a well-structured eigen-system. We propose to estimate theExpand
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Predictive Computational Modeling of the Mucosal Immune Responses during Helicobacter pylori Infection
T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding theExpand
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Active Learning Through Sequential Design, With Applications to Detection of Money Laundering
Money laundering is a process designed to conceal the true origin of funds that were originally derived from illegal activities. Because money laundering often involves criminal activities, financialExpand
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Scalable Algorithms for the Sparse Ridge Regression
TLDR
We first prove that the continuous relaxation of the mixed integer second order conic (MISOC) reformulation is equivalent to that of the convex integer formulation proposed in recent work. Expand
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Design for computer experiments with qualitative and quantitative factors
TLDR
We introduce a new class of designs, called marginally coupled designs, for computer experiments with both qualitative and quantitative variables. Expand
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A Parallel Implementation of the Ensemble Kalman Filter Based on Modified Cholesky Decomposition
TLDR
This paper discusses an efficient parallel implementation of the ensemble Kalman filter based on the modified Cholesky decomposition; the estimates are computed concurrently on separate processors. Expand
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The CCP Selector: Scalable Algorithms for Sparse Ridge Regression from Chance-Constrained Programming
Sparse regression and variable selection for large-scale data have been rapidly developed in the past decades. This work focuses on sparse ridge regression, which considers the exact $L_0$ norm toExpand
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