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Short-term cancer incidence predicti...

Fan, Jiaquan.

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Short-term cancer incidence prediction with missing data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Short-term cancer incidence prediction with missing data./
Author:	Fan, Jiaquan.
Description:	126 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-03, Section: B, page: 1388.
Contained By:	Dissertation Abstracts International65-03B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3124601

Short-term cancer incidence prediction with missing data.
Fan, Jiaquan.

Short-term cancer incidence prediction with missing data. - 126 p.

Source: Dissertation Abstracts International, Volume: 65-03, Section: B, page: 1388.

Thesis (Ph.D.)--The George Washington University, 2004.

Data on cancer incidence, which is the number of new cancer cases diagnosed, are collected from cancer registries in the United States. Such data suffer from a serious missing-data problem due to the lack of a nationwide cancer registry.Subjects--Topical Terms:

517247
Statistics.

Short-term cancer incidence prediction with missing data.
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100 1 $a Fan, Jiaquan. $3 1949873
245 1 0 $a Short-term cancer incidence prediction with missing data.
300 $a 126 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-03, Section: B, page: 1388.
500 $a Director: Kaushik Ghosh.
502 $a Thesis (Ph.D.)--The George Washington University, 2004.
520 $a Data on cancer incidence, which is the number of new cancer cases diagnosed, are collected from cancer registries in the United States. Such data suffer from a serious missing-data problem due to the lack of a nationwide cancer registry.
520 $a In this dissertation, we investigate the problem of short-term incidence prediction from both temporal and spatial points of view. Our proposed method has three parts. First, the missing incidence data are imputed using the back-calculation method. Second, structural time series models are fit to the observed and estimated incidence to make temporal projection. Finally, for states without any incidence data, we use a locally weighted smoother to spatially predict the incidence based on the incidence predictions for other states. The proposed methods are tested on real datasets and are shown to give interpretable and improved predictions over the currently used methods.
590 $a School code: 0075.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Health Sciences, Public Health. $3 1017659
690 $a 0463
690 $a 0308
690 $a 0573
710 2 0 $a The George Washington University. $3 1017405
773 0 $t Dissertation Abstracts International $g 65-03B.
790 1 0 $a Ghosh, Kaushik, $e advisor
790 $a 0075
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3124601