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Methods for Improved Small Area Estimation in Forest Inventory
Methods for Improved Small Area Estimation in Forest Inventory
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105312
- ISBN
- 9798265450906
- DDC
- 634.9
- 서명/저자
- Methods for Improved Small Area Estimation in Forest Inventory
- 발행사항
- [Sl] : Michigan State University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 74 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: A.
- 주기사항
- Advisor: Finley, Andrew O.
- 학위논문주기
- Thesis (Ph.D.)--Michigan State University, 2025.
- 초록/해제
- 요약This dissertation investigates and introduces methods for improved estimation of forest attributes in small geographic regions. A central focus is placed on two-stage hierarchical models that account for the zero-inflation that is commonly found in forest inventory variables, but estimation in areas that do not exhibit zero-inflation is also considered. Further, a nonparametric method of simulating artificial populations for comparison small area estimators is introduced. This method produces artificial populations that resemble the structure and composition of forests by using a bootstrap-weighted k nearest neighbor algorithm in auxiliary data space and allows for "fair" comparison of parametric small area estimators due to its nonparametric nature. The proposed small area estimators are compared using the simulation framework introduced in this work and through unit-level cross validation. Statistical software packages are developed to facilitate implementation of the introduced methods for researchers, practitioners, and other interested parties.
- 일반주제명
- Forestry
- 일반주제명
- Geography
- 일반주제명
- Environmental science
- 일반주제명
- Wildlife management
- 키워드
- Forest inventory
- 키워드
- Small area
- 키워드
- Auxiliary data
- 기타저자
- Michigan State University Forestry - Doctor of Philosophy
- 기본자료저록
- Dissertations Abstracts International. 87-06A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202105312
■006m o d
■007cr#unu||||||||
■020 ▼a9798265450906
■035 ▼a(MiAaPQ)AAI32284856
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a634.9
■1001 ▼aWhite, Grayson W.
■24510▼aMethods for Improved Small Area Estimation in Forest Inventory
■260 ▼a[Sl]▼bMichigan State University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a74 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: A.
■500 ▼aAdvisor: Finley, Andrew O.
■5021 ▼aThesis (Ph.D.)--Michigan State University, 2025.
■520 ▼aThis dissertation investigates and introduces methods for improved estimation of forest attributes in small geographic regions. A central focus is placed on two-stage hierarchical models that account for the zero-inflation that is commonly found in forest inventory variables, but estimation in areas that do not exhibit zero-inflation is also considered. Further, a nonparametric method of simulating artificial populations for comparison small area estimators is introduced. This method produces artificial populations that resemble the structure and composition of forests by using a bootstrap-weighted k nearest neighbor algorithm in auxiliary data space and allows for "fair" comparison of parametric small area estimators due to its nonparametric nature. The proposed small area estimators are compared using the simulation framework introduced in this work and through unit-level cross validation. Statistical software packages are developed to facilitate implementation of the introduced methods for researchers, practitioners, and other interested parties.
■590 ▼aSchool code: 0128.
■650 4▼aForestry
■650 4▼aGeography
■650 4▼aEnvironmental science
■650 4▼aWildlife management
■653 ▼aSmall geographic regions
■653 ▼aForest inventory
■653 ▼aSmall area
■653 ▼aAuxiliary data
■690 ▼a0478
■690 ▼a0366
■690 ▼a0768
■690 ▼a0286
■71020▼aMichigan State University▼bForestry - Doctor of Philosophy.
■7730 ▼tDissertations Abstracts International▼g87-06A.
■790 ▼a0128
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360153▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
