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Aggregation of Affine Estimators

It is shown that the proposed estimator leads simultaneously to all the best known bounds for aggregation, including $\ell_q$-aggregation, $q \in (0,1)$, with high probability.
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QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION.

This paper proposes a novel Rayleigh quotient based sparse quadratic dimension reduction method-named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization)-for analyzing high-dimensional data and proposes an efficient linearized augmented Lagrangian method to solve the constrained optimization problem.
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Unifying approach to selective inference with applications to cross-validation

We develop tools to do valid post-selective inference for a family of model selection procedures, including choosing a model via cross-validated Lasso. The tools apply universally when the following…
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A Projection-based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models.

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A Conditional Dependence Measure with Applications to Undirected Graphical Models

Measuring conditional dependence is an important topic in statistics with broad applications including graphical models. Under a factor model setting, a new conditional dependence measure is…

Neyman-Pearson classification: parametrics and sample size requirement

A new parametric thresholding algorithm is proposed, which does not need the minimum sample size requirements on class $0$ observations and thus is suitable for small sample applications such as rare disease diagnosis and proves NP oracle inequalities for one proposed classifier.
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Inferactive data analysis

The approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis, and the basis of inference is (a conditional approach to) selective inference, in particular its randomized form.
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Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman–Pearson Approach to Text Classification

This article addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion and proposes the use of the Neyman–Pearson (NP) classification paradigm, which minimizes Type II error under a user-specified Type I error constraint.
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Comparison of prediction errors: Adaptive p-values after cross-validation

A novel variation to these model selection procedures by adding Gaussian randomizations to either one of the two vectors, illustrating the method through adding randomization and applying the technical tools to four important procedures for which no valid selective inference has yet developed.

Intentional control of type I error over unconscious data distortion: a Neyman-Pearson classification approach

This work proposes a new distortion-invariant statistical approach to parse data, based on the Neyman-Pearson (NP) classification paradigm, and illustrates the working of this new method by combining the recently developed NP umbrella algorithm with topic modeling to automatically detect posts that are related to strikes and corruption in samples of randomly selected posts extracted from Sina Weibo.
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