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Missing at random (MAR) in nonparametric regression - A simulation experiment

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  • Thomas Nittner

    (Ludwig-Maximilians-Universität)

Abstract

. The additive model $y = s(x) + \epsilon$ is considered when some observations on x are missing at random but corresponding observations on y are available. Especially for this model, missing at random is an interesting case because the complete case analysis is expected to be no more suitable. A simulation experiment is reported and the different methods are compared based on their superiority with respect to the sample mean squared error. Some focus is also given on the sample variance and the estimated bias. In detail, the complete case analysis, a kind of stochastic mean imputation, a single imputation and the nearest neighbor imputation are discussed.

Suggested Citation

  • Thomas Nittner, 2003. "Missing at random (MAR) in nonparametric regression - A simulation experiment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(2), pages 195-210, December.
    • Handle: RePEc:spr:stmapp:v:12:y:2003:i:2:d:10.1007_s10260-003-0054-2
    DOI: 10.1007/s10260-003-0054-2
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    Cited by:

    1. Nengxiang Ling & Rui Kan & Philippe Vieu & Shuyu Meng, 2019. "Semi-functional partially linear regression model with responses missing at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 39-70, January.
    2. Bowen Sun & Haomin Li & Qiuyun Zhao, 2018. "Logistics agglomeration and logistics productivity in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(2), pages 273-293, September.
    3. Nengxiang Ling & Lilei Cheng & Philippe Vieu & Hui Ding, 2022. "Missing responses at random in functional single index model for time series data," Statistical Papers, Springer, vol. 63(2), pages 665-692, April.
    4. Salim Bouzebda & Youssouf Souddi & Fethi Madani, 2024. "Weak Convergence of the Conditional Set-Indexed Empirical Process for Missing at Random Functional Ergodic Data," Mathematics, MDPI, vol. 12(3), pages 1-22, January.

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