Data-Driven Smooth Goodness of Fit Test by Nonparametric Function Estimation

  • Published : 2000.12.01

Abstract

The purpose of this paper is to study of data-driven smoothing goodness of it test, when the hypothesis is complete. The smoothing goodness of fit test statistic by nonparametric function estimation techniques is proposed in this paper. The results of simulation studies for he powers of show that the proposed test statistic compared well to other.

References

  1. Journal of American Statistical Association v.89 Data-Driven Version of Neyman's Smooth Test of Fit Ledwina, T.
  2. Annals of Statistics v.20 Asymptotic Comparison of Cramer-von Mises and Nonparametric Function Estimation Techniques for Testing Goodness-of-Fit Eubank, R.L.;LaRiccia, V.N.
  3. Journal of American Statistical Association v.91 Test significance Based on Wavelet Thresholding and Neyman's Truncation Fan, J.
  4. Testing Goodness of Fit : A New Approach, in Non-parametric Statistics and Related Topics Bickel, P.J.;Ritov, Y.;A.K.Md.E. Saleh(ed.)
  5. Goodness-of-Fit Techniques D'Agostino R.B.;Stephens M.A.
  6. Nonparamtric Smoothing and Lack-of-Fit Tests Hart, J.D.
  7. Skand. Aktuar v.20 Smooth Tests for Goodness of Fit Neyman, J.
  8. Journal of American Statistical Association v.95 Testing Goodness-of-Fit via Order Section Criteria Kim, J.T.
  9. The Annals of Statistics v.20 Testing Goodness-of-Fit in Regression via Order Selection Criteria Eubank, R.L.;Hark, J.D.
  10. Journal of American Statistical Association v.92 Data-Dirven Smooth Tests When the Hypothesis Is Complete Kallenberg, W.C.M.;Ledwina, T.
  11. Biometrika v.80 Commonality of Cusum, von Neumann and Smoothing-Based Goodness-of-Fit Tests Eubank, R.L.;Hart, J.D.