JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Bayesian Model Selection for Nonlinear Regression under Noninformative Prior
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Bayesian Model Selection for Nonlinear Regression under Noninformative Prior
Na, Jonghwa; Kim, Jeongsuk;
  PDF(new window)
 Abstract
We propose a Bayesian model selection procedure for nonlinear regression models under noninformative prior. For informative prior, Na and Kim (2002) suggested the Bayesian model selection procedure through MCMC techniques. We extend this method to the case of noninformative prior. The difficulty with the use of noninformative prior is that it is typically improper and hence is defined only up to arbitrary constant. The methods, such as Intrinsic Bayes Factor(IBF) and Fractional Bayes Factor(FBF), are used as a resolution to the problem. We showed the detailed model selection procedure through the specific real data set.
 Keywords
Nonlinear Regression;Noninformative prior;IBF;FBF;Importance Sampling;
 Language
Korean
 Cited by
 References
1.
Biometrika, 1978. vol.65. pp.39-48 crossref(new window)

2.
Nonlinear Regression Analysis and its Applications, 1988.

3.
Journal of the American Statistical Association, 1996. vol.91. pp.109-121 crossref(new window)

4.
Research Report 93-006, University of Minnesota, 1993.

5.
Sankhya, 1976. vol.38. pp.315-328

6.
Biometrika, 1983. vol.70. 2, pp.373-379 crossref(new window)

7.
Theory of Probability (3rd ed.), 1961.

8.
Journal of the American Statistical Association, 1995. vol.90. pp.377-395

9.
Technical Report 279, University of Washington, 1994.

10.
M.S. Thesis, University of Wisconsin-Madison, 1967.

11.
The Korean Journal of Applied Statistics, 2002. vol.15. 1, pp.139-151 crossref(new window)

12.
Journal of the Royal Statistical Society, Ser. B, 1995. vol.57. pp.99-138

13.
Journal of the Royal Statistical Society. Ser. B, 1982. vol.44. pp.377-387