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Generalized weighted additive models based on distribution functions

Authors
Yeo, In-Kwon
Issue Date
1-Jul-2007
Publisher
ELSEVIER SCIENCE BV
Keywords
Bayesian inference; Markov chain mome Carlo; beta mixtures; parametric transformation
Citation
STATISTICS & PROBABILITY LETTERS, v.77, no.12, pp.1394 - 1402
Journal Title
STATISTICS & PROBABILITY LETTERS
Volume
77
Number
12
Start Page
1394
End Page
1402
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/14664
DOI
10.1016/j.spl.2007.03.029
ISSN
0167-7152
Abstract
In this paper, a new form of generalized additive models is proposed. The proposed models consist of a distribution function of the mean response and a weighted linear combination of distribution functions of covariates. This form does not make any structural problems on linking the mean response and covariates. Markov chain Monte Carlo methods are used to estimate the parameters within a Bayesian framework. (c) 2007 Elsevier B.V. All rights reserved.
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