Nonparametric Methods for Complex Multivariate Data: Asymptotics and Small Sample Approximations
Yue Cui, Solomon W. Harrar

TL;DR
This paper introduces nonparametric methods for analyzing complex multivariate data, especially in the context of quality of life outcomes, providing robust effect size measures and asymptotic inference that handle missing data and clustering.
Contribution
It develops novel nonparametric effect size estimators with asymptotic properties, addressing challenges like missing data and clustering in multivariate analysis.
Findings
Effective in presence of missing values and clustering
Asymptotic properties of estimators established
Illustrated with asthma intervention trial data
Abstract
Quality of Life (QOL) outcomes are important in the management of chronic illnesses. In studies of efficacies of treatments or intervention modalities, QOL scales multi-dimensional constructs are routinely used as primary endpoints. The standard data analysis strategy computes composite (average) overall and domain scores, and conducts a mixed-model analysis for evaluating efficacy or monitoring medical conditions as if these scores were in continuous metric scale. However, assumptions of parametric models like continuity and homoscedastivity can be violated in many cases. Furthermore, it is even more challenging when there are missing values on some of the variables. In this paper, we propose a purely nonparametric approach in the sense that meaningful and, yet, nonparametric effect size measures are developed. We propose estimator for the effect size and develop the asymptotic…
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Taxonomy
TopicsStatistical Methods and Inference
