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Semiparametric estimation with missing covariates

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  • Bravo, Francesco

Abstract

This paper considers estimation in semiparametric models when some of the covariates are missing at random. The paper proposes an iterative estimator based on inverse probability weighting and local linear estimation of the nonparametric component. The resulting estimator is very general and can be used in the context of semiparametric maximum likelihood, quasi likelihood and robust estimation. The paper establishes the asymptotic normality of the estimator using both nonparametric and parametric estimation of the unknown probability weights. Two general examples illustrate the theory and Monte Carlo simulations show that the proposed estimator has good finite sample properties.

Suggested Citation

  • Bravo, Francesco, 2015. "Semiparametric estimation with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 329-346.
    • Handle: RePEc:eee:jmvana:v:139:y:2015:i:c:p:329-346
    DOI: 10.1016/j.jmva.2015.03.012
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    Cited by:

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    2. Francesco Bravo, 2020. "Robust estimation and inference for general varying coefficient models with missing observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 966-988, December.
    3. Timothy Reese & Majid Mojirsheibani, 2017. "On the $$L_p$$ L p norms of kernel regression estimators for incomplete data with applications to classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 81-112, March.
    4. Eric Han & Majid Mojirsheibani, 2021. "On histogram-based regression and classification with incomplete data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 635-662, July.
    5. Mojirsheibani, Majid & Shaw, Crystal, 2018. "Classification with incomplete functional covariates," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 40-46.

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