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New procedures for data mining and m...

Wang, Xiaofeng.

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New procedures for data mining and measurement error models with medical imaging applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	New procedures for data mining and measurement error models with medical imaging applications./
作者:	Wang, Xiaofeng.
面頁冊數:	177 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2664.
Contained By:	Dissertation Abstracts International66-05B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3176595
ISBN:	0542157675

New procedures for data mining and measurement error models with medical imaging applications.
Wang, Xiaofeng.

New procedures for data mining and measurement error models with medical imaging applications. - 177 p.

Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2664.

Thesis (Ph.D.)--Case Western Reserve University, 2005.

In this dissertation we provide analysis strategies for two research areas: spatial-temporal data mining and measurement error problems. Motivated by analyzing data from a "Neuromuscular Electrical Stimulation" experiment we develop an efficient procedure for mining spatial-temporal data which combines the following modern and newly developed components: data segmentation and registration, statistical smoothing mapping for identifying "activated" regions and a semiparametric model for detecting spatial-temporal similarities/trends from "large-p-small-n" data sets. For measurement error problems we provide new density and regression estimators for nonparametric errors-in-variables models. The errors can be either homogeneous or nonhomogeneous. In contrast to most existing procedures our new estimators are stable, easy to compute and do not depend on a Fourier transform. The asymptotics of the new estimators is investigated. Our procedures have the potential to become powerful new tools in the image analysis and other fields.

ISBN: 0542157675Subjects--Topical Terms:

517247
Statistics.

New procedures for data mining and measurement error models with medical imaging applications.
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520 $a In this dissertation we provide analysis strategies for two research areas: spatial-temporal data mining and measurement error problems. Motivated by analyzing data from a "Neuromuscular Electrical Stimulation" experiment we develop an efficient procedure for mining spatial-temporal data which combines the following modern and newly developed components: data segmentation and registration, statistical smoothing mapping for identifying "activated" regions and a semiparametric model for detecting spatial-temporal similarities/trends from "large-p-small-n" data sets. For measurement error problems we provide new density and regression estimators for nonparametric errors-in-variables models. The errors can be either homogeneous or nonhomogeneous. In contrast to most existing procedures our new estimators are stable, easy to compute and do not depend on a Fourier transform. The asymptotics of the new estimators is investigated. Our procedures have the potential to become powerful new tools in the image analysis and other fields.
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