首页|A GMM approach in coupling internal data and external summary information with heterogeneous data populations
A GMM approach in coupling internal data and external summary information with heterogeneous data populations
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万方数据
维普
Because of advances in data collection and storage,statistical analysis in modern scientific research and practice now has opportunities to utilize external information such as summary statistics from similar studies.A likelihood approach based on a parametric model assumption has been developed in the literature to utilize external summary information when the populations for external and main internal data are assumed to be the same.In this article,we instead consider the generalized estimation equation(GEE)approach for statistical inference,which is semiparametric or nonparametric,and show how to utilize external summary information even when internal and external data populations are not the same.Our approach is coupling the internal data and external summary information to form additional estimation equations and then applying the generalized method of moments(GMM).We show that the proposed GMM estimator is asymptotically normal and,under some conditions,is more efficient than the GEE estimator without using external summary information.Estimators of the asymptotic covariance matrix of the GMM estimators are also proposed.Simulation results are obtained to confirm our theory and quantify the improvements by utilizing external data.An example is also included for illustration.
adjustment for heterogeneityconstraintdata integrationgeneralized method of momentssummary statistic
Jun Shao、Jinyi Wang、Lei Wang
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Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University),Ministry of Education,Shanghai 200062,China
School of Statistics,East China Normal University,Shanghai 200062,China
Department of Statistics,University of Wisconsin-Madison,Madison,WI 53706,USA
School of Statistics and Data Science,KLMDASR,LEBPS and LPMC,Nankai University,Tianjin 300071,China
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National Natural Science Foundation of ChinaNational Science Foundation of USAFundamental Research Funds for the Central Universities and National Natural Science Foundation of China
万方数据
维普
