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On local estimating equations in additive multiparameter models

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  • Claeskens, Gerda
  • Aerts, Marc

Abstract

Estimating all parameters in a multiparameter response model as smooth functions of an explanatory variable is very similar to estimating the different components of an additive model for the response mean. It is shown that, in a general estimating framework, local polynomial backfitting estimators in an additive one-parameter model do not work optimally. For a multiparameter model, however, a backfitting algorithm can be defined that leads to local polynomial estimators that do have optimal properties.

Suggested Citation

  • Claeskens, Gerda & Aerts, Marc, 2000. "On local estimating equations in additive multiparameter models," Statistics & Probability Letters, Elsevier, vol. 49(2), pages 139-148, August.
    • Handle: RePEc:eee:stapro:v:49:y:2000:i:2:p:139-148
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    References listed on IDEAS

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    1. A. C. Davison & N. I. Ramesh, 2000. "Local likelihood smoothing of sample extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 191-208.
    2. Yuan, Ke-Hai & Jennrich, Robert I., 1998. "Asymptotics of Estimating Equations under Natural Conditions," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 245-260, May.
    3. Opsomer, Jan & Ruppert, David, 1997. "Fitting a Bivariate Additive Model by Local Polynomial Regression," Staff General Research Papers Archive 1071, Iowa State University, Department of Economics.
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