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Improved Neymanian analysis for 2 K factorial designs with binary outcomes
Author(s) -
Lu Jiannan
Publication year - 2019
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/stan.12186
Subject(s) - variance (accounting) , estimator , mathematics , statistics , factorial experiment , factorial , binary number , sampling (signal processing) , bias of an estimator , identification (biology) , minimum variance unbiased estimator , econometrics , computer science , arithmetic , mathematical analysis , botany , accounting , filter (signal processing) , business , computer vision , biology
2 K factorial designs are widely adopted by statisticians and the broader scientific community. In this short note, under the potential outcomes framework, we adopt the partial identification approach and derive the sharp lower bound of the sampling variance of the estimated factorial effects, which leads to an “improved” Neymanian variance estimator that mitigates the overestimation issue suffered by the classic Neymanian variance estimator.