@article{046820200fda41e6be98627cda2b2e6d,
title = "A Bayesian approach to flexible modeling of multivariable response functions",
abstract = "This paper presents a Bayesian approach to empirical regression modeling in which the response function is represented by a power series expansion in Hermite polynomials. The common belief that terms of low degree will reasonably approximate the response function is reflected by assigning prior distributions that exponentially downweight the coefficients of high-degree terms. The model thus includes the complete series expansion. A useful property of the Hermite expansion is that it can be easily extended to handle models with several explanatory variables.",
keywords = "Bayesian linear model, Hermite polynomials, empirical modeling, polynomial regression, smoothing",
author = "Steinberg, \{David M.\}",
note = "Funding Information: This work is based on the author{\textquoteright}s Ph.D. dissertation written under the direction of Professor G. E. P. Box. It is a pleasure to thank Professor Box for his many valuable comments. I also thank Professor R. Myers, for permission to use the data cited in Section 4, and a referee, for pointing out an error in an earlier.version of the proof in the Appendix. This research was sponsored by the United States Army under Contract DAAG29-80-C-0041 and by the National Science Foundation under Grant MCS-8210950.",
year = "1990",
month = aug,
doi = "10.1016/0047-259X(90)90033-E",
language = "אנגלית",
volume = "34",
pages = "157--172",
journal = "Journal of Multivariate Analysis",
issn = "0047-259X",
publisher = "Academic Press Inc.",
number = "2",
}