TY - JOUR
T1 - Optimal weighted two-sample t-test with partially paired data in a unified framework
AU - Guo, Xu
AU - Wang, Yan
AU - Zhou, Niwen
AU - Zhu, Xuehu
N1 - Funding Information:
The research described herewith was supported by National Natural Science Foundation of China [grant number 11701034], [grant number 61877049], the Fundamental Research Funds for the Central Universities, and China Postdoctoral Science Foundation [grant number 2017M610058], [grant number 2016M590934], [grant number 2017T100731]. The authors are grateful to the Editor, the associate editor, and the two anonymous referees for substantive comments that have significantly improved this manuscript.
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - In this paper, we provide a unified framework for two-sample t-test with partially paired data. We show that many existing two-sample t-tests with partially paired data can be viewed as special members in our unified framework. Some shortcomings of these t-tests are discussed. We also propose the asymptotically optimal weighted linear combination of the test statistics comparing all four paired and unpaired data sets. Simulation studies are used to illustrate the performance of our proposed asymptotically optimal weighted combinations of test statistics and compare with some existing methods. It is found that our proposed test statistic is generally more powerful. Three real data sets about CD4 count, DNA extraction concentrations, and the quality of sleep are also analyzed by using our newly introduced test statistic.
AB - In this paper, we provide a unified framework for two-sample t-test with partially paired data. We show that many existing two-sample t-tests with partially paired data can be viewed as special members in our unified framework. Some shortcomings of these t-tests are discussed. We also propose the asymptotically optimal weighted linear combination of the test statistics comparing all four paired and unpaired data sets. Simulation studies are used to illustrate the performance of our proposed asymptotically optimal weighted combinations of test statistics and compare with some existing methods. It is found that our proposed test statistic is generally more powerful. Three real data sets about CD4 count, DNA extraction concentrations, and the quality of sleep are also analyzed by using our newly introduced test statistic.
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U2 - 10.1080/02664763.2020.1753027
DO - 10.1080/02664763.2020.1753027
M3 - Article
C2 - 35707734
AN - SCOPUS:85083633607
SN - 0266-4763
VL - 48
SP - 961
EP - 976
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 6
ER -