Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Dan L. Nicolae, Xiao-Li Meng, Augustine Kong
TL;DR
This paper discusses methods for quantifying the impact of missing data on hypothesis testing in statistical and genetic research, emphasizing the importance of accurately measuring information loss.
Contribution
It provides a detailed discussion and clarification on the methods for quantifying missing information in hypothesis testing, building upon prior work and addressing existing challenges.
Findings
Clarifies the methodology for measuring missing information
Highlights the importance of accurate information quantification
Addresses challenges in genetic and statistical hypothesis testing
Abstract
Rejoinder to "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
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