東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Statistical Modeling of Multivariate...

Tidemann-Miller, Beth A.

Linked to FindBook

Google Book

Amazon

博客來

Statistical Modeling of Multivariate Functional Data that Exhibit Complex Correlation Structures.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Modeling of Multivariate Functional Data that Exhibit Complex Correlation Structures./
Author:	Tidemann-Miller, Beth A.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2014,
Description:	98 p.
Notes:	Source: Dissertations Abstracts International, Volume: 76-07, Section: B.
Contained By:	Dissertations Abstracts International76-07B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3647697
ISBN:	9781321414479

Statistical Modeling of Multivariate Functional Data that Exhibit Complex Correlation Structures.
Tidemann-Miller, Beth A.

Statistical Modeling of Multivariate Functional Data that Exhibit Complex Correlation Structures. - Ann Arbor : ProQuest Dissertations & Theses, 2014 - 98 p.

Source: Dissertations Abstracts International, Volume: 76-07, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2014.

This item must not be sold to any third party vendors.

Due to the large size of modern data sets, there is an ever-increasing need for computationally efficient inferential methods designed for realistic models of large observed functional data sets. The first part of this dissertation introduces an innovative modeling framework for the analysis of multivariate functional data, where each individual functional component exhibits multilevel and spatial structures. The proposed methodology uses a functional principal components based approach for multivariate functional data, which has important advantages in the dimensionality reduction of the data and brings considerable computational savings. Moreover, our approach quantifies the spatial auto- and cross-correlation between units at the lowest level of the hierarchy. The proposed procedure is illustrated through simulation studies and data from a colon carcinogenesis experimental study. In the second part of the dissertation, we propose a Bayesian modeling framework for jointly analyzing multiple functional responses of different types (e.g. binary and continuous data). Our approach is based on a multivariate latent Gaussian process and models the dependence among the functional responses through the dependence of the latent process. Our framework easily accommodates additional covariates. We offer a way to estimate the multivariate latent covariance, allowing for implementation of multivariate functional principal components analysis to specify basis expansions and simplify computation. We demonstrate our method through both simulation studies and an application to real data from a periodontal study.

ISBN: 9781321414479Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Binary and categorical functional responses

Statistical Modeling of Multivariate Functional Data that Exhibit Complex Correlation Structures.
LDR:02951nmm a2200385 4500 001 2274627
005 20201202130037.5
008 220629s2014 ||||||||||||||||| ||eng d
020 $a 9781321414479
035 $a (MiAaPQ)AAI3647697
035 $a AAI3647697
040 $a MiAaPQ $c MiAaPQ
100 1 $a Tidemann-Miller, Beth A. $3 3552126
245 1 0 $a Statistical Modeling of Multivariate Functional Data that Exhibit Complex Correlation Structures.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2014
300 $a 98 p.
500 $a Source: Dissertations Abstracts International, Volume: 76-07, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Reich, Brian;Staicu, Ana-Maria.
502 $a Thesis (Ph.D.)--North Carolina State University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Due to the large size of modern data sets, there is an ever-increasing need for computationally efficient inferential methods designed for realistic models of large observed functional data sets. The first part of this dissertation introduces an innovative modeling framework for the analysis of multivariate functional data, where each individual functional component exhibits multilevel and spatial structures. The proposed methodology uses a functional principal components based approach for multivariate functional data, which has important advantages in the dimensionality reduction of the data and brings considerable computational savings. Moreover, our approach quantifies the spatial auto- and cross-correlation between units at the lowest level of the hierarchy. The proposed procedure is illustrated through simulation studies and data from a colon carcinogenesis experimental study. In the second part of the dissertation, we propose a Bayesian modeling framework for jointly analyzing multiple functional responses of different types (e.g. binary and continuous data). Our approach is based on a multivariate latent Gaussian process and models the dependence among the functional responses through the dependence of the latent process. Our framework easily accommodates additional covariates. We offer a way to estimate the multivariate latent covariance, allowing for implementation of multivariate functional principal components analysis to specify basis expansions and simplify computation. We demonstrate our method through both simulation studies and an application to real data from a periodontal study.
590 $a School code: 0155.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
650 4 $a Oncology. $3 751006
653 $a Binary and categorical functional responses
653 $a Bivariate functional data
653 $a Bivariate spatial modeling
653 $a Functional principal components analysis
653 $a Multivariate
690 $a 0308
690 $a 0463
690 $a 0992
710 2 $a North Carolina State University. $3 1018772
773 0 $t Dissertations Abstracts International $g 76-07B.
790 $a 0155
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3647697