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NETWORK EXPLORATION VIA THE ADAPTIVE LASSO AND SCAD PENALTIES.

Non-concave penalties and the adaptive LASSO penalty are introduced to attenuate the bias problem in the network estimation to solve the problem of precision matrix estimation.
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Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models

This work shows that with general nonparametric models, under some mild technical conditions, the proposed independence screening methods have a sure screening property and the extent to which the dimensionality can be reduced by independence screening is also explicitly quantified.
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A road to classification in high dimensional space: the regularized optimal affine discriminant

A delicate result on continuous piecewise linear solution paths for the ROAD optimization problem at the population level justifies the linear interpolation of the constrained co‐ordinate descent algorithm.
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IMILAST: A Community Effort to Intercompare Extratropical Cyclone Detection and Tracking Algorithms

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen…
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High-dimensional variable selection for Cox's proportional hazards model

This work extends the sure screening procedure to Cox's proportional hazards model with an iterative version available and demonstrates the utility and versatility of the iterative sure independence screening scheme.
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STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation

The Speech-TExt Manifold Mixup (STEMM) method effectively alleviates the cross-modal representation discrepancy, and achieves significant improvements over a strong baseline on eight translation directions.
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Neyman-Pearson classification algorithms and NP receiver operating characteristics

This work develops the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, such as logistic regression, support vector machines, and random forests, and proposes a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands motivated by the popular ROC curves.
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North Atlantic wave height trends as reconstructed from the 20th century reanalysis

This study reports on the 1871–2010 trends in significant wave heights (Hs) in the North Atlantic, as statistically reconstructed from the 20th century reanalysis (20CR) ensemble of mean sea level…
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Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification

FANS is a high-dimensional classification method that involves nonparametric feature augmentation that is related to generalized additive models, but has better interpretability and computability.
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Local quasi-likelihood with a parametric guide.

This work proposes two parametrically guided nonparametric estimation schemes by incorporating prior shape information on the link transformation of the response variable's conditional mean in terms of the predictor variable.
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