A Comparative Study on the Performance of Bayesian Partially Linear Models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 우윤성 | - |
dc.contributor.author | 최태련 | - |
dc.contributor.author | 김우석 | - |
dc.date.accessioned | 2021-09-07T03:06:41Z | - |
dc.date.available | 2021-09-07T03:06:41Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/110335 | - |
dc.description.abstract | In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 한국통계학회 | - |
dc.title | A Comparative Study on the Performance of Bayesian Partially Linear Models | - |
dc.title.alternative | A Comparative Study on the Performance of Bayesian Partially Linear Models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 최태련 | - |
dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.19, no.6, pp.885 - 898 | - |
dc.relation.isPartOf | Communications for Statistical Applications and Methods | - |
dc.citation.title | Communications for Statistical Applications and Methods | - |
dc.citation.volume | 19 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 885 | - |
dc.citation.endPage | 898 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001713962 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Partially linear models | - |
dc.subject.keywordAuthor | Fourier series | - |
dc.subject.keywordAuthor | Gaussian process priors | - |
dc.subject.keywordAuthor | smoothness | - |
dc.subject.keywordAuthor | root mean squared error. | - |
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