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Robust doubly protected estimators for quantiles with missing data

Mariela Sued (marielasued@gmail.com), Marina Valdora (mvaldora@gmail.com) and Víctor Yohai (vyohai@dm.uba.ar)
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Mariela Sued: Universidad de Buenos Aires and CONICET
Marina Valdora: Universidad de Buenos Aires
Víctor Yohai: Universidad de Buenos Aires and CONICET

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2020, vol. 29, issue 3, No 10, 819-843

Abstract: Abstract Doubly protected methods are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for the missing responses, a vector $$\mathbf {X}$$ X of covariates is observed at each individual, and the missing mechanism is assumed to be independent of the response, conditioned on $$\mathbf {X}$$ X (missing at random). In recent years, many authors have turned from the estimation of the mean to that of the median, and more generally, doubly protected estimators of the quantiles have been proposed. In this work, we present doubly protected estimators for the quantiles in semiparametric models that are also robust, in the sense that they are resistant to the presence of outliers in the sample.

Keywords: Robust estimators; Missing data; Median; Quantiles; Propensity score; Doubly protected estimators; Primary 62F35; Secondary 62F12 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11749-019-00689-9

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