Koul, Hira L.; Müller, Ursula U.; Schick, Anton The transfer principle: a tool for complete case analysis. (English) Zbl 1296.62040 Ann. Stat. 40, No. 6, 3031-3049 (2012). Summary: This paper gives a general method for deriving limiting distributions of complete case statistics for missing data models from corresponding results for the model where all data are observed. This provides a convenient tool for obtaining the asymptotic behavior of complete case versions of established full data methods without lengthy proofs. { } The methodology is illustrated by analyzing three inference procedures for partially linear regression models with responses missing at random. We first show that complete case versions of asymptotically efficient estimators of the slope parameter for the full model are efficient, thereby solving the problem of constructing efficient estimators of the slope parameter for this model. 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