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Bayesian hierarchical modeling of ge...

University of Minnesota.

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Bayesian hierarchical modeling of geostatistical and spatial point process data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian hierarchical modeling of geostatistical and spatial point process data./
作者:	Liang, Shengde.
面頁冊數:	102 p.
附註:	Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 5864.
Contained By:	Dissertation Abstracts International69-10B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3334426
ISBN:	9780549871606

Bayesian hierarchical modeling of geostatistical and spatial point process data.
Liang, Shengde.

Bayesian hierarchical modeling of geostatistical and spatial point process data. - 102 p.

Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 5864.

Thesis (Ph.D.)--University of Minnesota, 2008.

Spatially referenced data occur in diverse scientific disciplines. Researchers' interests are often related to certain response levels at arbitrary or specific points or areas. Statistical theory and methods to analyze such data depend upon these configurations and have enjoyed significant developments over the last decade. The Minnesota Cancer Surveillance System (MCSS) has collected person-specific data including the residential address of essentially every cancer patient in the state of Minnesota. We develop various Bayesian models to answer various questions related to these data from 1998-2002. First, we propose a multiresolution logistic regression model to answer questions about the preference of surgery for northern Minnesota women with breast cancer. Next, we analyze colon and rectum cancer occurrence data from the Twin Cities metro area cinder the point-process framework, extending it to incorporate both spatial and non-spatial covariates. We then extend our work on point-process model to Bayesian wombling (spatial boundary analysis), and use this to analyze prostate and colorectal cancer cases from the northern portion of the state.

ISBN: 9780549871606Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Bayesian hierarchical modeling of geostatistical and spatial point process data.
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520 $a Spatially referenced data occur in diverse scientific disciplines. Researchers' interests are often related to certain response levels at arbitrary or specific points or areas. Statistical theory and methods to analyze such data depend upon these configurations and have enjoyed significant developments over the last decade. The Minnesota Cancer Surveillance System (MCSS) has collected person-specific data including the residential address of essentially every cancer patient in the state of Minnesota. We develop various Bayesian models to answer various questions related to these data from 1998-2002. First, we propose a multiresolution logistic regression model to answer questions about the preference of surgery for northern Minnesota women with breast cancer. Next, we analyze colon and rectum cancer occurrence data from the Twin Cities metro area cinder the point-process framework, extending it to incorporate both spatial and non-spatial covariates. We then extend our work on point-process model to Bayesian wombling (spatial boundary analysis), and use this to analyze prostate and colorectal cancer cases from the northern portion of the state.
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