Nonparametric regression using Bayesian variable selection

Journal of Econometrics

Volumn 75, Issue 2, 1996, Pages 317-343

  Smith, Michael ^a   Kohn, Robert ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Additive model; Gibbs sampler; Power transformation; Regression spline; Robust estimation

Indexed keywords

EID: 0000824232     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/0304-4076(95)01763-1     Document Type: Article

References (23)

1. Belsley D.A., E. Kuh, and R. Welsch, 1980, Regression diagnostics (Wiley, New York, NY).
2. Box, G.E.P. and D.R. Cox, 1964, An analysis of transformations, Journal of the Royal Statistical Society B 26, 211-252.
3. Box, G.E.P. and D.R. Cox, 1982, An analysis of transformations revisited, rebutted, Journal of the American Statistical Association 77, 209-210.
4. Box, G.E.P. and G.C. Tiao, 1968, A Bayesian approach to some outlier problems, Biometrika 55, 119-129.
5. Breiman, L. and J.H. Friedman, 1985, Estimating optimal transformations for multiple regression and Correlation, Journal of the American Statistical Association 80, 580-598.
6. Cleveland, W.S. and E. Grosse, 1991, Computational methods for local regression, Statistics and Computing 1, 47-62.
7. Cleveland, W.S., E. Grosse, and W.M. Shyu, 1992, Local regression models, in: J.M. Chambers and T.J. Hastie, Statistical models in S (Wadsworth, Pacific Grove, CA) 309-373.
8. Dongarra, J.J., C.B. Moler, J.R. Bunch, and G.W. Stewart, 1979, Linpack users' guide (Siam, Philadelphia, PA).
9. Emerson, J.D. and M.A. Stoto, 1983, Transforming data, in: D.C. Hoaglin, F. Mosteller, and J.W. Tauchen, eds., Understanding robust and exploratory data analysis (Wiley, New York, NY) 97-128.
10. Eubank, R.L., 1988, Spline smoothing and nonparametric regression (Marcel Dekker, New York, NY).
11. Friedman, J.H. and B.W. Silverman, 1989, Flexible parsimonious smoothing and additive modeling, Technometrics 31, 3-39.
12. Gelfand, A.E. and A.F.M. Smith, 1990, Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association 85, 398-409.
13. George, E.I. and R.E. McCulloch, 1993, Variable selection via Gibbs sampling, Journal of the American Statistical Association 88, 881-889.
14. George, E.I. and R.E. McCulloch, 1994, Fast Bayes variable selection, Preprint.
15. Gu, C. and G. Wahba, 1991, Minimising GCV/GML scores with multiple smoothing parameters via the Newton method, SIAM Journal of Scientific and Statistical Computing 12, 383-398.
16. Harrison, D. and D.L. Rubinfeld, 1978, Hedonic housing prices and demand for clean air, Journal of Environmental Economics and Management 5, 81-102.
17. Hastie, T.J. and R.J. Tibshirani, 1990, Generalized additive models (Chapman Hall, New York, NY).
18. Mitchell, T.J. and J.J. Beauchamp, 1988, Bayesian variable selection in linear regression, Journal of the American Statistical Association 83, 1023-1036.
19. Raftery, A., D. Madigan, and J. Hoeting, 1993, Model selection and accounting for model uncertainty in linear regression models, Working paper (University of Washington, Seattle, WA).
20. Ruppert D., S.J. Sheather, and M.P. Wand, 1995, An effective bandwidth selector for local least squares regression, Journal of the American Statistical Association 90, 1257-1270.
21. Sweeting, T.J., 1985, Consistent prior distributions for transformed models, in: J.M. Bernardo, D.V. Lindely, and A.F.M. Smith, eds., Bayesian statistics 2 (North-Holland, Amsterdam) 755-762.
22. Verdinelli, I. and L. Wasserman, 1991, Bayesian analysis of outlier problems using the Gibbs sampler, Statistics and Computing 1, 105-117.
23. Zellner, A., 1986, On assessing prior distributions and Bayesian regression analysis with g-prior distributions, in: P.K. Goel and A. Zellner, Bayesian inference and decision techniques - Essays in honor of Bruno de Finetti (North-Holland, Amsterdam) 233-243.