التفاصيل البيبلوغرافية
| العنوان: |
Robust inference in generalized partially linear models |
| المؤلفون: |
Boente, Graciela gboente@dm.uba.ar, Rodriguez, Daniela drodrig@dm.uba.ar |
| المصدر: |
Computational Statistics & Data Analysis. Dec2010, Vol. 54 Issue 12, p2942-2966. 25p. |
| مصطلحات موضوعية: |
*INFERENCE (Logic), *ROBUST control, *LINEAR statistical models, *MATHEMATICAL models, *DATA analysis, *SMOOTHNESS of functions, *ESTIMATION theory |
| مستخلص: |
Abstract: In many situations, data follow a generalized partly linear model in which the mean of the responses is modeled, through a link function, linearly on some covariates and nonparametrically on the remaining ones. A new class of robust estimates for the smooth function , associated to the nonparametric component, and for the parameter , related to the linear one, is defined. The robust estimators are based on a three-step procedure, where large values of the deviance or Pearson residuals are bounded through a score function. These estimators allow us to make easier inferences on the regression parameter and also improve computationally those based on a robust profile likelihood approach. The resulting estimates of turn out to be root- consistent and asymptotically normally distributed. Besides, the empirical influence function allows us to study the sensitivity of the estimators to anomalous observations. A robust Wald test for the regression parameter is also provided. Through a Monte Carlo study, the performance of the robust estimators and the robust Wald test is compared with that of the classical ones. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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