Improved Auxiliary Mixture Sampling for Hierarchical Models of Non-Gaussian Data.pdfVIP

下载本文档

4
0
约4.94万字
约 28页
2017-04-13 发布于江苏
举报
版权申诉

Improved Auxiliary Mixture Sampling for Hierarchical Models of Non-Gaussian Data.pdf

1、有哪些信誉好的足球投注网站（book118）网站文档一经付费（服务费），不意味着购买了该文档的版权，仅供个人/单位学习、研究之用，不得用于商业用途，未经授权，严禁复制、发行、汇编、翻译或者网络传播等，侵权必究。。
2、本站所有内容均由合作方或网友上传，本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺！文档内容仅供研究参考，付费前请自行鉴别。如您付费，意味着您自己接受本站规则且自行承担风险，本站不退款、不进行额外附加服务；查看《如何避免下载的几个坑》。如果您已付费下载过本站文档，您可以点击这里二次下载。
3、如文档侵犯商业秘密、侵犯著作权、侵犯人身权等，请点击“版权申诉”（推荐），也可以打举报电话：400-050-0827(电话支持时间：9:00-18:30)。
4、该文档为VIP文档，如果想要下载，成为VIP会员后，下载免费。
5、成为VIP后，下载本文档将扣除1次下载权益。下载后，不支持退款、换文档。如有疑问请联系我们。
6、成为VIP后，您将拥有八大权益，权益包括：VIP文档下载权益、阅读免打扰、文档格式转换、高级专利检索、专属身份标志、高级客服、多端互通、版权登记。
7、VIP文档为合作方或网友上传，每下载1次，网站将根据用户上传文档的质量评分、类型等，对文档贡献者给予高额补贴、流量扶持。如果你也想贡献VIP文档。上传文档

Improved Auxiliary Mixture Sampling for Hierarchical Models of Non-Gaussian Data

Improved Auxiliary Mixture Sampling for Hierarchical Models of Non-Gaussian Data Sylvia Fru?hwirth-Schnattera, Rudolf Fru?hwirthb, Leonhard Heldc and H?avard Rued aDepartment of Applied Statistics and Econometrics Johannes Kepler Universita?t Linz, Austria bInstitute of High Energy Physics Austrian Academy of Sciences, Vienna, Austria cInstitute of Social and Preventive Medicine University of Zurich, Switzerland d Department of Mathematical Sciences Norwegian University of Science and Technology, Trondheim, Norway May 29, 2007 Abstract The article proposes an improved method of auxiliary mixture sampling for count data, binomial data and multinomial data. In constrast to previously proposed samplers the method uses a limited number of latent variables per observation, independent of the intensity of the underlying Poisson process in the case of count data, or of the number of experiments in the case of binomial and multinomial data. The smaller number of latent variables results in a more general error distribution, which is a negative log-Gamma distribution with arbitray integer shape parameter. The required approximations of these distributions by Gaussian mixtures have been computed. Overall, the improvement leads to a substantial increase in efficiency of auxiliary mixture sampling for highly structured models. The method is illustrated on two epidemiological case studies. Key words: Count data, Binomial data, Disease mapping, Gaussian mixture, Log-Gamma distribution, Multinomial data Submitted to Journal of Computational and Graphical Statistics 1 Introduction During the past years, auxiliary mixture sampling has turned out to be a useful tool for the Bayesian analysis of hierarchical and parameter-driven models of non-Gaussian data. The method has been used first by Shephard (1994) for stochastic volatility models and has been applied in this context by a couple of authors (Kim et al., 1998; Chib et al., 2002; Omori et al., 2004). Recently, auxiliary mixture s