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Found 4 papers, 0 papers with code
Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification
To deal with inestimable data distortion, we propose the use of the Neyman-Pearson (NP) classification paradigm, which minimizes type II error under a user-specified type I error constraint.
Neyman-Pearson classification: parametrics and sample size requirement
In this work, we employ the parametric linear discriminant analysis (LDA) model and propose a new parametric thresholding algorithm, 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.
A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models
Measuring conditional dependence is an important topic in statistics with broad applications including graphical models.
Aggregation of Affine Estimators
While results indicate that the same aggregation scheme may not satisfy sharp oracle inequalities with high probability, we prove that a weaker notion of oracle inequality for EW that holds with high probability.
