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Likelihood-based kernel estimation in semiparametric errors-in-covariables models with validation data

Listed author(s):
  • Wang, Qihua
  • Yu, Keming
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    We present methods to handle error-in-variables models. Kernel-based likelihood score estimating equation methods are developed for estimating conditional density parameters. In particular, a semiparametric likelihood method is proposed for sufficiently using the information in the data. The asymptotic distribution theory is derived. Small sample simulations and a real data set are used to illustrate the proposed estimation methods.

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    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 98 (2007)
    Issue (Month): 3 (March)
    Pages: 455-480

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    Handle: RePEc:eee:jmvana:v:98:y:2007:i:3:p:455-480
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    1. C.-Y. Wang & Margaret Sullivan Pepe, 2000. "Expected estimating equations to accommodate covariate measurement error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 509-524.
    2. Zhang, Biao, 1997. "Empirical likelihood confidence intervals for M-functionals in the presence of auxiliary information," Statistics & Probability Letters, Elsevier, vol. 32(1), pages 87-97, February.
    3. Wang, Qihua & Härdle, Wolfgang, 2002. "Empirical likelihood-based dimension reduction inference for linear error-in-responses models with validation study," SFB 373 Discussion Papers 2002,82, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Wang, Qihua, 2000. "Estimation of Linear Error-in-Covariables Models with Validation Data Under Random Censorship," Journal of Multivariate Analysis, Elsevier, vol. 74(2), pages 245-266, August.
    5. Wang, Qihua, 1999. "Estimation of Partial Linear Error-in-Variables Models with Validation Data," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 30-64, April.
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