2 citations · 9 across the 8 of their papers we have counts for
9 papers
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes
Lee Friedman, Timothy Hanson, Hal S. Stern +1
Spatial precision is often measured using the standard deviation (SD) of the eye position signal or the RMS of the sample-to-sample differences (StoS) signal during fixation. As bo…
A Dirichlet Process Mixture Model for Directional-Linear Data
Tong Zou, Hal S. Stern
Directional data require specialized probability models because of the non-Euclidean and periodic nature of their domain. When a directional variable is observed jointly with linea…
Nonparametric tests for treatment effect heterogeneity in observational studies
Maozhu Dai, Weining Shen, Hal S. Stern
We consider the problem of testing for treatment effect heterogeneity in observational studies, and propose a nonparametric test based on multisample U-statistics. To account for p…
A U-statistic-based test of treatment effect heterogeneity
Maozhu Dai, Hal S. Stern
Many studies include a goal of determining whether there is treatment effect heterogeneity across different subpopulations. In this paper, we propose a U-statistic-based non-parame…
Why Temporal Persistence of Biometric Features is so Valuable for Classification Performance
Lee Friedman, Hal Stern, Larry R. Price +1
It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although t…
The Linear Relationship between Temporal Persistence, Number of Independent Features and Target EER
Lee Friedman, Hal S. Stern, Oleg V. Komogortsev
If you have a target level of biometric performance (e.g. EER = 5% or 0.1%), how many units of unique information (uncorrelated features) are needed to achieve that target? We show…