東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Statistical Methods for Complex Spatial Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Methods for Complex Spatial Data./
作者:	Kim, Minho.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	100 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647715
ISBN:	9798538130047

Statistical Methods for Complex Spatial Data.
Kim, Minho.

Statistical Methods for Complex Spatial Data. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 100 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Baylor University, 2021.

This item is not available from ProQuest Dissertations & Theses.

Spatial analysis is an active research area as it allows us to solve problemscontaining geographic information in various applications. In this dissertation, weconsider some challenging issues we often face in practice. The work of this dissertationmainly focuses on spatial binary data. Binary data contains much lessinformation than that of continuous type, which hinders our ability to obtain accuratepredictions. To tackle this issue, we present a Bayesian downscaling model usingspatially varying coecients, which allows us to make inferences at high resolutionfrom low resolution observed data. We also consider a situation where the binary datais measured with some errors, causing presence of misclassication in the data. Inpractice, misclassication is a well known problem, but often is ignored and analysisis performed as if data is measured perfectly. We address this issue by presenting aspatial misclassication model.While high resolution data may be superior in spatial coverage, it often suersfrom a considerable number of censored observations due to a limit of detection of adevice. To properly handle this issue, a statistical method with a predictor subjectto censoring is presented. In addition, we relax a linearity assumption between aresponse and a predictor variable to increase the exibility of modeling. We examineeach model by performing extensive simulation studies and illustrate with real worldapplications using precipitation data in South Korea.

ISBN: 9798538130047Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Bayesian

Statistical Methods for Complex Spatial Data.
LDR:02665nmm a2200373 4500 001 2343876
005 20220513114340.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798538130047
035 $a (MiAaPQ)AAI28647715
035 $a AAI28647715
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kim, Minho. $3 1297898
245 1 0 $a Statistical Methods for Complex Spatial Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 100 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Song, Joon Jin.
502 $a Thesis (Ph.D.)--Baylor University, 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a Spatial analysis is an active research area as it allows us to solve problemscontaining geographic information in various applications. In this dissertation, weconsider some challenging issues we often face in practice. The work of this dissertationmainly focuses on spatial binary data. Binary data contains much lessinformation than that of continuous type, which hinders our ability to obtain accuratepredictions. To tackle this issue, we present a Bayesian downscaling model usingspatially varying coecients, which allows us to make inferences at high resolutionfrom low resolution observed data. We also consider a situation where the binary datais measured with some errors, causing presence of misclassication in the data. Inpractice, misclassication is a well known problem, but often is ignored and analysisis performed as if data is measured perfectly. We address this issue by presenting aspatial misclassication model.While high resolution data may be superior in spatial coverage, it often suersfrom a considerable number of censored observations due to a limit of detection of adevice. To properly handle this issue, a statistical method with a predictor subjectto censoring is presented. In addition, we relax a linearity assumption between aresponse and a predictor variable to increase the exibility of modeling. We examineeach model by performing extensive simulation studies and illustrate with real worldapplications using precipitation data in South Korea.
590 $a School code: 0014.
650 4 $a Statistics. $3 517247
653 $a Bayesian
653 $a Censoring
653 $a Generalized additive model
653 $a Hybrid model
653 $a Misclassification
653 $a Spatial binary
690 $a 0463
710 2 $a Baylor University. $b Statistical Science. $3 1681241
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0014
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647715