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

Author

Listed:
  • Ana M. Bianco

    (Universidad de Buenos Aires and CONICET Ciudad Universitaria)

  • Graciela Boente

    (Universidad de Buenos Aires and IMAS, CONICET Ciudad Universitaria)

  • Wenceslao González-Manteiga

    (Universidad de Santiago de Compostela)

  • Ana Pérez-González

    (Universidad de Vigo)

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 $$\alpha $$ 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.

Suggested Citation

  • Ana M. Bianco & Graciela Boente & Wenceslao González-Manteiga & Ana Pérez-González, 2019. "Plug-in marginal estimation under a general regression model with missing responses and covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 106-146, March.
    • Handle: RePEc:spr:testjl:v:28:y:2019:i:1:d:10.1007_s11749-018-0591-5
    DOI: 10.1007/s11749-018-0591-5
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    References listed on IDEAS

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