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Estimation of generalized partially linear models with measurement error using sufficiency scores

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  • Liu, Lian

Abstract

We study the partially linear model in logistic and other types of canonical exponential family regression when the explanatory variable is measured with independent normal error. We develop a backfitting estimation procedure to this model based upon the parametric idea of sufficiency scores so that no assumptions are made about the latent variable measured with error. We derive the method's asymptotic properties and present a numerical example and a simulation study.

Suggested Citation

  • Liu, Lian, 2007. "Estimation of generalized partially linear models with measurement error using sufficiency scores," Statistics & Probability Letters, Elsevier, vol. 77(15), pages 1580-1588, September.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:15:p:1580-1588
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    References listed on IDEAS

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    1. Maity, Arnab & Ma, Yanyuan & Carroll, Raymond J., 2007. "Efficient Estimation of Population-Level Summaries in General Semiparametric Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 123-139, March.
    2. Gerda Claeskens & Raymond J. Carroll, 2007. "An asymptotic theory for model selection inference in general semiparametric problems," Biometrika, Biometrika Trust, vol. 94(2), pages 249-265.
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    Cited by:

    1. Qianqian Wang & Yanyuan Ma & Guangren Yang, 2020. "Locally efficient estimation in generalized partially linear model with measurement error in nonlinear function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 553-572, June.

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