Sequential adaptive variables and subject selection for GEE methods

doi:10.1111/biom.13160

Sequential adaptive variables and subject selection for GEE methods

2019-11-11

0

PRE

AI

DOI：10.1111/biom.13160

原文链接

原文求助

摘要

En 中文

AbstractModeling correlated or highly stratified multiple‐response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time‐consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active‐learning methods found in the machine‐learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.

Sequential adaptive variables and subject selection for GEE methods

摘要

AI总结

期刊

机构

引用论文