Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 325 - Analysis of Bioscience Data
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: International Chinese Statistical Association
Abstract #312507
Title: Nonparametric Effect Measures in Multivariate Growth Data: Applications in Optometry
Author(s): Ting Zeng* and Solomon Harrar
Companies: University of Kentucky and University of Kentucky
Keywords: relative effects; Wald-type test; ranks; repeated measures; factorial design; ordinal data
Abstract:

Optometric variables such as visual acuity, waveforms and gaze position are measured in ordered categorical scales. They involve multiple variables that are measured multiple times on each subject. The usual mean- and covariance-based inferences are not appropriate for such dataset. We present general nonparametric methods for multivariate repeated measure data in factorial design. Treatment effects are characterized in terms of functionals of distribution functions with the sole assumption of nondegenerate marginal distributions. This model accommodates binary, categorical, discrete and continuous data in a unified manner. Hypotheses are formulated in terms of meaningful nonparametric measures of treatment effects. Wald-type statistic is proposed and its asymptotic properties are investigated. The theory can also be used to construct confidence intervals for the treatment effects. Simulations are conducted to show the finite-sample performance of the proposed methods in comparison with other parametric, nonparametric methods. Data from a study of infantile nystagmus syndrome(INS) investigating the interaction effect between patients’ mental load and gaze position will be analyzed.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program