# On Varieties Of Doubly Robust Estimators Under Missingness Not At Random With A Shadow Variable

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by J Zhao 2020 the true missingness mechanism tends to be missing not at random, e.g., patient the covariate X. In general, a shadow variable with a smaller E.J. On varieties of doubly robust estimators under missingness not at random.

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### CURRICULUM VITAE Wang Miao

On varieties of doubly robust estimators under missingness not at random with a shadow variable. Biometrika, 103:475 482. 3. Miao W., Geng Z., and Tchetgen

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by T Hoshino 2019 moments in random sampling data in order to correct the bias. doubly robust-type estimator for biased sampling data. which there is no missingness in variables of interest y. We conduct two types of simulation studies when auxiliary information is Shadow prices, market wages, and labor supply.

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by J Zhao 2020 We adopt the shadow variable approach in this paper primarily by virtue of missing-not-at-random (MNAR). We term Z the Miao, W. and Tchetgen Tchetgen, E. J. On varieties of doubly robust estimators under missingness not at random.

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by Ò Jordà 2013 Cited by 367 one half followed the prescriptions of the doctors of the variety of those The boom, not the slump, is the right time for austerity at the Treasury. into the broad class of doubly robust estimators of which Robins, Rotnitzky and Zhao (1994) section, Dt is a continuous random variable although later in the

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of some covariates, not of intrinsic interest, in order to estimate the values of incomplete variables and the propensity to have them observed, even if these auxiliary 2.1 Estimating a population mean from incomplete data under missing at random E.J. (2016) On varieties of doubly robust estimators under missingness.

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by M Che 2020 Cited by 1 Summary: For regression with covariates missing not at random (MNAR) where the missingness depends on the missing probability conditional on all fully observed variables in the data set. With this model On varieties of doubly robust estimators under missingness not at random with a shadow variable. Biometrika

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by X Wang 2020 propose a doubly robust estimator to unbiasedly measure the error of 4.2 The types of intent hierarchies in TREC Web Track 2009-2013 test collec- a critical problem is that the missing ratings are often missing not at random dom variable in a label (output) space Y. Let prpy xq be the real data Shadow Man (‹).

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