Permutation tests for general dependent truncation

Permutation Tests for Classification

We introduce and explore an approach to estimating statistical significance of classification accuracy, which is particularly useful in scientific applications of machine learning where high dimensionality of the data and the small number of training examples render most standard convergence bounds too loose to yield a meaningful guarantee of the generalization ability of the classifier. Instea...

Permutation tests for nonparametric detection

In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman-Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the ...

Permutation Tests for Linear Models

Several approximate permutation tests have been proposed for tests of partial regression coefficients in a linear model based on sample partial correlations. This paper begins with an explanation and notation for an exact test. It then compares the distributions of the test statistics under the various permutation methods proposed, and shows that the partial correlations under permutation are a...

Permutation Tests for Infection Graphs

We formulate and analyze a hypothesis testing problem for inferring the edge structure of an infection graph. Our model is as follows: A disease spreads over a network via contagion and random infection, where uninfected nodes contract the disease at a time corresponding to an independent exponential random variable and infected nodes transmit the disease to uninfected neighbors according to in...

Mantel Permutation Tests