Cross-validating non-gaussian data: Generalized approximate cross-validation revisited

Journal of Computational and Graphical Statistics

Volumn 10, Issue 3, 2001, Pages 581-591

  Gu, Chong ^a   Xiang, Dong ^b  

^a PURDUE UNIVERSITY   (United States)

^b SAS INSTITUTE INC   (United States)

Author keywords

Kullback leibler loss; Penalized likelihood; Smoothing parameter

Indexed keywords

EID: 0035622815     PISSN: 10618600     EISSN: 15372715     Source Type: Journal    
DOI: 10.1198/106186001317114992     Document Type: Article

References (18)

1. Cox, D. D., and Chang, Y. (1990), “Iterated State Space Algorithms and Cross Validation for Generalized Smoothing Splines,” Technical Report 49, Department of Statistics, University of Illinois, Champion, IL.
2. Craven, P., and Wahba, G. (1979), “Smoothing Noisy Data With Spline Functions: Estimating the Correct Degree of Smoothing by the Method of Generalized Cross-Validation,” Numerische Mathematik, 31, 377-403.
3. Green, P., and Silverman, B. W. (1994), Nonparametric Regression and Generalized Linear Models, London: Chapman and Hall.
4. Green, P., and Yandell, B. (1985), “Semi-Parametric Generalized Linear Models,” in Proceedings of the GLIM85 Conference, New York: Springer-Verlag, pp. 44-55.
5. Gu, C. (1989), “Rkpack and its Applications: Fitting Smoothing Spline Models,” in ASA Proceedings ofStatistical Computing Section, Alexandria, VA: American Statisticial Association, pp. 42-51.
6. Gu, C. (1990), “Adaptive Spline Smoothing in Non-Gaussian Regression Models,” Journal of the American Statistical association, 85, 801-807.
7. Gu, C. (1992a), “Cross-Validating Non-Gaussian Data,” Journal of Computational and Graphical Statistics 1, 169-179.
8. Gu, C. (1992b), “Penalized Likelihood Regression: A Bayesian Analysis,” Statistica Sinica, 2, 255-264.
9. Gu, C. (1993), “Smoothing Spline Density Estimation: A Dimensionless Automatic Algorithm,” Journal of American Statistical Association, 88, 495-504.
10. Gu, C. (1998), “Structural Multivariate Function Estimation: Some Automatic Density and Hazard Estimates,” Statistica Sinica, 8, 317-335.
11. Hutchinson, M. F., and de Hoog, F. R. (1985), “Smoothing Noisy Data With Spline Functions,” Numerische Mathematik, 47, 99-106.
12. Lin, X., Wahba, G., Xiang, D., Gao, F., Klein, R., and Klein, B. (1998), “Smoothing Spline ANOVA Models for Large Data Sets With Bernoulli Observations and the Randomized GACV,” Technical Report 998, Department of Statistics, University of Wisconsin, Madison, WI.
13. McCullagh, P., and Nelder, J. A. (1989), Generalized Linear Models (2nd ed.), London: Chapman and Hall.
14. O’Sullivan, F. (1985), Discussion of “Some Aspects of the Spline Smoothing Approach to Nonparametric Re gression Curve Fitting” by B. Silverman, Journal of the Royal Statistical Society, Ser. B, 47, 39-40.
15. O’Sullivan, F., Yandell, B., and Raynor, W. (1986), “Automatic Smoothing of Regression Functions in Generalized Linear Models,” Journal of the American Statistical Association, 81, 96-103.
16. Silverman, B. W. (1978), “Density Ratios, Empirical Likelihood and Cot Death,” Applied Statistics, 27, 26-33.
17. Wahba, G. (1990), Spline Models for Observational Data, Volume 59 ofCBMS-NSF Regional Conference Series in Applied Mathematics, Philadelphia: SIAM.
18. Xiang, D., and Wahba, G. (1996), “A Generalized Approximate Cross Validation for Smoothing Splines With non-Gaussian Data,” Statistica Sinica, 6, 675-692