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Wavelet-based estimation for Gaussian time series and spatio-temporal processes- [electronic resource]
Wavelet-based estimation for Gaussian time series and spatio-temporal processes- [electronic resource]
상세정보
- 자료유형
- 학위논문(국외)
- 자관 청구기호
- 기본표목-개인명
- 표제와 책임표시사항
- Wavelet-based estimation for Gaussian time series and spatio-temporal processes - [electronic resource] / Zheng, Wenjun.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 1 online resource(168 p)
- 일반주기
- Source: Dissertation Abstracts International, Volume: 77-06(E), Section: B.
- 일반주기
- Adviser: Peter Craigmile.
- 학위논문주기
- Thesis (Ph.D.)--The Ohio State University, 2014.
- 요약 등 주기
- 요약Modern statistical analyses often require the modeling non-Markov time series and spatio-temporal dependencies. Traditional likelihood methods are computationally demanding for these models, leading us to consider approximate likelihood methods that are computationally efficient, while not overly compromising on the efficiency of the parameter estimates. In this dissertation various wavelet-based Whittle approximations are investigated to model a certain class of nonstationary Gaussian time series and a class of Gaussian spatio-temporal processes. Wavelet transforms can help decorrelate processes across and within wavelet scales, allowing for the simplified modeling of time series and spatio-temporal processes. In addition to being computationally efficient, the proposed maximum wavelet-Whittle likelihood estimators of a Gaussian process are shown to be asymptotically normal. Asymptotic properties of the estimators are verified in simulation studies, demonstrating that the typical independence everywhere assumption assumed for wavelet-based estimation is not optimal. These methods are applied to the analyses of a Southern Oscillation Index climate series and the Irish wind speed data.
- 주제명부출표목-일반주제명
- 부출표목-단체명
- 기본자료저록
- Dissertation Abstracts International. 77-06B(E).
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 원문정보보기
- 소장사항
-
20180515 2018
MARC
008180601s2014 us esm 001c eng■001MOKWON01260320
■00520180518093159
■007cr
■020 ▼a9781339416625
■035 ▼a(MiAaPQ)AAI10001914
■035 ▼a(MiAaPQ)OhioLINK:osu1405608102
■040 ▼aMiAaPQ▼cMiAaPQ
■090 ▼a전자도서(박사논문)
■1001 ▼aZheng, Wenjun.
■24510▼aWavelet-based estimation for Gaussian time series and spatio-temporal processes▼h[electronic resource]▼cZheng, Wenjun.
■260 ▼a[Sl]▼bThe Ohio State University▼c2014
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2014
■300 ▼a1 online resource(168 p)
■500 ▼aSource: Dissertation Abstracts International, Volume: 77-06(E), Section: B.
■500 ▼aAdviser: Peter Craigmile.
■5021 ▼aThesis (Ph.D.)--The Ohio State University, 2014.
■520 ▼aModern statistical analyses often require the modeling non-Markov time series and spatio-temporal dependencies. Traditional likelihood methods are computationally demanding for these models, leading us to consider approximate likelihood methods that are computationally efficient, while not overly compromising on the efficiency of the parameter estimates. In this dissertation various wavelet-based Whittle approximations are investigated to model a certain class of nonstationary Gaussian time series and a class of Gaussian spatio-temporal processes. Wavelet transforms can help decorrelate processes across and within wavelet scales, allowing for the simplified modeling of time series and spatio-temporal processes. In addition to being computationally efficient, the proposed maximum wavelet-Whittle likelihood estimators of a Gaussian process are shown to be asymptotically normal. Asymptotic properties of the estimators are verified in simulation studies, demonstrating that the typical independence everywhere assumption assumed for wavelet-based estimation is not optimal. These methods are applied to the analyses of a Southern Oscillation Index climate series and the Irish wind speed data.
■590 ▼aSchool code: 0168.
■650 4▼aStatistics
■690 ▼a0463
■71020▼aThe Ohio State University▼bStatistics.
■7730 ▼tDissertation Abstracts International▼g77-06B(E).
■773 ▼tDissertation Abstract International
■790 ▼a0168
■791 ▼aPh.D.
■792 ▼a2014
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T14821437▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a20180515▼f2018
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