Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies

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Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies

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Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies.

The authors suggest an interesting way to measure the fraction of missing information in the context of hypothesis testing. The measure seeks to quantify the impact of missing observations on the test between two hypotheses. The amount of impact can be useful information for applied research. An example is, in genetics, where multiple tests of the same sort are performed on different variables ...

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Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies

Many practical studies rely on hypothesis testing procedures applied to data sets with missing information. An important part of the analysis is to determine the impact of the missing data on the performance of the test, and this can be done by properly quantifying the relative (to complete data) amount of available information. The problem is directly motivated by applications to studies, such...

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ژورنال

عنوان ژورنال: Statistical Science

سال: 2008

ISSN: 0883-4237

DOI: 10.1214/08-sts244rej