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Essays in Empirical Bayes Methods
Essays in Empirical Bayes Methods
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151058
- ISBN
- 9798382830032
- DDC
- 310
- 저자명
- Ho, Sheng Chao.
- 서명/저자
- Essays in Empirical Bayes Methods
- 발행사항
- [Sl] : University of Pennsylvania, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 147 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Cheng, Xu;Schorfheide, Frank.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2024.
- 초록/해제
- 요약This dissertation develops empirical Bayes methods for large-scale estimation. Researchers are now interested in leveraging increasingly large datasets for estimation of heterogeneous parameters, in fields ranging from teacher value-added estimation to developing optimal large-scale forecasts, where the number of parameters is typically in the thousands. The problem of estimating teacher value-added serves as the running example throughout the dissertation, and as such also forms the basis of both chapters' empirical applications.The first chapter develops shrinkage estimation that is robust to unobserved sorting between multiple dimensions of fixed effects. We motivate an estimator class through a hierarchical Bayes perspective that is shown to nest the common shrinkage estimators, such as the one-way fixed effects estimator prominent in the teacher value-added literature. We provide an algorithm of optimizing within this class that results in a feasible estimator that is asymptotically optimal within the entire class. We then consider extending the estimator class in empirically relevant directions, such as in modeling sorting directly through the prior that further sharpens estimation.The second chapter addresses the problem of optimal estimation under unknown heteroskedasticity, where we consider a generalization of the objective from the usual unit-specific means to now unit-specific quantiles. The unknown heteroskedasticity renders the common shrinkage estimators infeasible, because the optimal amount of shrinkage depends precisely on the unknown sampling variance. We provide an extension of the Tweedie's formula and show that the compound optimal mean and quantile estimators depend only on density of certain sufficient statistics. We then exploit this to propose feasible, asymptotic compound optimal estimators.
- 일반주제명
- Statistics
- 키워드
- Bayes methods
- 기타저자
- University of Pennsylvania Economics
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798382830032
■035 ▼a(MiAaPQ)AAI31142654
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aHo, Sheng Chao.
■24510▼aEssays in Empirical Bayes Methods
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a147 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Cheng, Xu;Schorfheide, Frank.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2024.
■520 ▼aThis dissertation develops empirical Bayes methods for large-scale estimation. Researchers are now interested in leveraging increasingly large datasets for estimation of heterogeneous parameters, in fields ranging from teacher value-added estimation to developing optimal large-scale forecasts, where the number of parameters is typically in the thousands. The problem of estimating teacher value-added serves as the running example throughout the dissertation, and as such also forms the basis of both chapters' empirical applications.The first chapter develops shrinkage estimation that is robust to unobserved sorting between multiple dimensions of fixed effects. We motivate an estimator class through a hierarchical Bayes perspective that is shown to nest the common shrinkage estimators, such as the one-way fixed effects estimator prominent in the teacher value-added literature. We provide an algorithm of optimizing within this class that results in a feasible estimator that is asymptotically optimal within the entire class. We then consider extending the estimator class in empirically relevant directions, such as in modeling sorting directly through the prior that further sharpens estimation.The second chapter addresses the problem of optimal estimation under unknown heteroskedasticity, where we consider a generalization of the objective from the usual unit-specific means to now unit-specific quantiles. The unknown heteroskedasticity renders the common shrinkage estimators infeasible, because the optimal amount of shrinkage depends precisely on the unknown sampling variance. We provide an extension of the Tweedie's formula and show that the compound optimal mean and quantile estimators depend only on density of certain sufficient statistics. We then exploit this to propose feasible, asymptotic compound optimal estimators.
■590 ▼aSchool code: 0175.
■650 4▼aStatistics
■653 ▼aBayes methods
■653 ▼aShrinkage estimation
■653 ▼aSampling variance
■653 ▼aLarge-scale estimation
■690 ▼a0501
■690 ▼a0463
■690 ▼a0511
■71020▼aUniversity of Pennsylvania▼bEconomics.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160671▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
