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Open Access Publications from the University of California

Goodness-of-Fit Test for Parametric Models

  • Author(s): Fan, Jianqing
  • Huang, Li-Shan
  • et al.

Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric fits is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates of contiguous alternatives that can be detected consistently by the adaptive Neyman test. Applications of the procedures to the partially linear models are thoroughly discussed. Our simulation studies show that the new testing procedures are indeed powerful and omnibus. The power of the proposed tests is comparable to the F-test statistic even in the situations where F-test is known to be suitable and can be far more powerful than the F-test statistic in other situations. An application to testing linear models versus additive models are addressed.

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