Parametrically Guided Non-parametric Regression

Scandinavian Journal of Statistics

Volumn 25, Issue 4, 1998, Pages 649-668

  Glad, Ingrid K ^a  

^a UNIVERSITY OF OSLO   (Norway)

Author keywords

Bias reduction; Correction factor; Kernel estimators; Local linear regression; Local polynomial regression; Semiparametric regression

Indexed keywords

EID: 0032352555     PISSN: 03036898     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9469.00127     Document Type: Article

References (25)

1. Abramson, I. S. (1982). On bandwidth variation in kernel estimates - a square root law. Ann. Statist. 10, 1217-1223.
2. Breiman, L. & Peters, S. (1992). Comparing automatic smoothers (a public service enterprise). Int. Statist. Rev. 60, 271-290.
3. Cleveland W. S. (1979). Robust locally weighted regression and smoothing scatter plots. J. Amer. Statist. Assoc. 74, 829-836.
4. Fan, J. (1992). Design-adaptive nonparametric regression. J. Amer. Statist. Assoc. 87, 998-1004.
5. Fan, J. (1993). Local linear regression smoothers and their minimax efficiencies. Ann. Statist. 21, 196-216.
6. Fan, J. & Gijbels, I. (1992). Variable bandwidth and local linear regression smoothers. Ann. Statist. 20, 2008-2036.
7. Fan, J. & Gijbels, I. (1996). Local polynomial modelling and its applications. Chapman & Hall, London.
8. Fan, Y. & Ullah, A. (1996). Asymptotic normality of a combined regression estimator. Manuscript, University of Windsor.
9. Gasser, T. & Müller, H. G. (1979). Kernel estimation of regression functions. Smoothing techniques for curve estimation. Lecture Notes in Mathematics 757, 23-68. Springer Verlag, New York.
10. Glad, I. K. (1996). A note on unconditional properties for a parametrically guided Nadaraya-Watson estimator. Statistical Research Report, Department of Mathematics, University of Oslo, No. 19 1996. To appear in Statist. Probab. Lett. (1998).
11. Glad, I. K. (1996). A note on unconditional properties for a parametrically guided Nadaraya-Watson estimator. Statistical Research Report, Department of Mathematics, University of Oslo, No. 19 1996. To appear in Statist. Probab. Lett. (1998).
12. Gozalo, P. & Linton, O. (1996). Using parametric information in nonparametric regression. Cowles Foundation Discussion Paper No. 1075.
13. Hastie, T. J. & Loader. C. (1993). Local regression: automatic kernel carpentry (with comments). Statist. Sci. 8, 120-143.
14. Hjort, N. L. & Glad, I. K. (1995). Nonpararmetric density estimation with a parametric start. Ann. Statist. 23, 882-904.
15. Hjort, N. L. & Glad, I. K. (1996). On the exact performance of a multiplicative semiparametric density estimator. Statistical Research Report, Department of Mathematics, University of Oslo, No. 8 1996. Submitted.
16. Jones, M. C., Davies, S. J. & Park, B. U. (1994). Versions of kernel-type regression estimators. J. Amer. Statist. Assoc. 89, 825-832.
17. Jones, M. C., Linton, O. & Nielsen, J. P. (1995). A simple and effective bias reduction method for density and regression estimation. Biometrika 82, 327-338.
18. Ruppert, D. & Wand. M. P. (1994). Multivariate locally weighted least squares regression. Ann. Statist. 22, 1346-1370.
19. Scott, D. W. (1992). Multivariate density estimation: theory, practice, and visualization. Wiley, New York.
20. Seifert, B. & Gasser, T. (1996). Finite-sample variance of local polynomials: Analysis and solutions. J. Amer. Statist. Assoc., 91, 267-275.
21. Serfling, R. J. (1980). Approximation theorems of mathematical statistics. Wiley, New York.
22. Smith, P. (1982). Curve fitting and modeling with splines using statistical variable selection techniques. NASA Contractor Report 166034.
23. Stone, C. J. (1977). Consistent nonparametric regression (with discussion). Ann. Statist. 5, 595-620.
24. Terrell, G. R. & Scott, D. W. (1992). Variable kernel density estimation. Ann. Statist. 20, 1236-1265.
25. Wand, M. P. & Jones, M. C. (1995). Kernel smoothing. Chapman & Hall, London.