Constructing a More Powerful Test in Two-Level Block Randomized Designs

Constructing a More Powerful Test in Two-Level Block Randomized Designs

A more powerful test for comparing two Poisson means

The problem of hypothesis testing about two Poisson means is addressed. The usual conditional test (C-test) and a test based on estimated p-values (E-test) are considered. The exact properties of the tests are evaluated numerically. Numerical studies indicate that the E-test is almost exact because its size seldom exceeds the nominal level, and it is more powerful than the C-test. Power calcula...

A More Powerful Two-Sample Test in High Dimensions using Random Projection

We consider the hypothesis testing problem of detecting a shift between the means of two multivariate normal distributions in the high-dimensional setting, allowing for the data dimension p to exceed the sample size n. Our contribution is a new test statistic for the two-sample test of means that integrates a random projection with the classical Hotelling T 2 statistic. Working within a high-di...

PoTrojan: powerful neural-level trojan designs in deep learning models

With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Artificial neural network or neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language processing. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs cou...

Beyond Sorting: A More Powerful Test for Cross-Sectional Anomalies

Many researchers seek factors that predict the cross-section of stock returns. The standard methodology sorts stocks according to their factor scores into quantiles and forms a corresponding long-short portfolio. Such a course of action ignores any information on the covariance matrix of stock returns. Historically, it has been difficult to estimate the covariance matrix for a large universe of...