Facultade de Fisioterapia

Plug-in marginal estimation under a general regression model with missing responses and covariates

Bianco, Ana M.; Boente, Graciela Boente; González-Manteiga, Wenceslao; Pérez González, Ana
Abstract:
In this paper, we consider a general regression model where missing data occur in the response and in the covariates. Our aim is to estimate the marginal distribution function and a marginal functional, such as the mean, the median or any α-quantile of the response variable. A missing at random condition is assumed in order to prevent from bias in the estimation of the marginal measures under a non-ignorable missing mechanism. We give two different approaches for the estimation of the responses distribution function and of a given marginal functional, involving inverse probability weighting and the convolution of the distribution function of the observed residuals and that of the observed estimated regression function. Through a Monte Carlo study and two real data sets, we illustrate the behaviour of our proposals. © 2018, Sociedad de Estadística e Investigación Operativa.
Year:
2019
Type of Publication:
Article
Keywords:
Fisher consistency; Kernel weights; L-estimators; Marginal functionals; Missing at random; Semiparametric models
Journal:
Test
Volume:
28
Number:
1
Pages:
106-146
Month:
March
Note:
Q2 42/123 h-index 1.423 (JCR2018)
Comments:
Acknowledgements The authors wish to thank two anonymous referees for valuable comments which led to an improved version of the original paper. This work was partially developed while Ana M. Bianco and Graciela Boente were visiting the Departamento de Estatística, Análise Matemática e Optimización de la Universidad de Santiago de Compostela, Spain under the bilateral agreement between the Universidad de Buenos Aires and the Universidad de Santiago de Compostela. This research was partially supported by Grants pict 2014-0351 from anpcyt and 20020130100279BA from the Universidad de Buenos Aires, Argentina and also by the Spanish Projects MTM2013-41383P and MTM2016-76969P from the Ministry of Science and Innovation, Spain. A. Bianco and G. Boente also wish to thank the Minerva Foundation for its support to present some of this paper results at the International Conference on Robust Statistics 2017.
DOI:
10.1007/s11749-018-0591-5
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