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Statistical Inference in Competitive Equilibrium
Statistical Inference in Competitive Equilibrium
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103506
- ISBN
- 9798280754805
- DDC
- 510
- 저자명
- Liao, Luofeng.
- 서명/저자
- Statistical Inference in Competitive Equilibrium
- 발행사항
- [Sl] : Columbia University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 234 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: A.
- 주기사항
- Advisor: Kroer, Christian.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2025.
- 초록/해제
- 요약This thesis studies statistical inference and A/B testing in settings with interference arising from competitive market effects. We study these effects in two fundamental market equilibrium models: the linear Fisher market (LFM) equilibrium and first-price pacing equilibrium (FPPE). LFM arises from fair resource allocation systems (such as physical allocation of food to food banks) and more generally distribution systems (such as user attention to different types of social media notifications on Instagram, or jobseekers' attention to job posts in LinkedIn). For LFM, we assume that the observed data is captured by the classical finite-dimensional Fisher market equilibrium, and its steady-state behavior is modeled by a continuous limit Fisher market. The second type of equilibrium we study, FPPE, arises from internet advertising applications, where advertisers are constrained by budgets and advertising opportunities are sold via first-price auctions. Pacing is a prevalent approach for managing advertiser budgets, where the platform assigns each advertiser an autobidder that controls expenditure by adaptively shading bids. For platforms that use pacing-based methods to smooth out the spending of advertisers, FPPE provides a steady-state description of the outcome of pacing-based markets.In Chapter 1 we develop a theory of statistical inference for LFMs and FPPE. In Chapter 2 we investigate theoretically sound bootstrap approaches for LFM and FPPE. In Chapter 3 we apply the theory to reduce interference bias in parallel A/B tests on ad auction platforms.
- 일반주제명
- Mathematics
- 일반주제명
- Information science
- 기타저자
- Columbia University Operations Research
- 기본자료저록
- Dissertations Abstracts International. 86-12A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202103506
■006m o d
■007cr#unu||||||||
■020 ▼a9798280754805
■035 ▼a(MiAaPQ)AAI32002722
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a510
■1001 ▼aLiao, Luofeng.
■24510▼aStatistical Inference in Competitive Equilibrium
■260 ▼a[Sl]▼bColumbia University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a234 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: A.
■500 ▼aAdvisor: Kroer, Christian.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2025.
■520 ▼aThis thesis studies statistical inference and A/B testing in settings with interference arising from competitive market effects. We study these effects in two fundamental market equilibrium models: the linear Fisher market (LFM) equilibrium and first-price pacing equilibrium (FPPE). LFM arises from fair resource allocation systems (such as physical allocation of food to food banks) and more generally distribution systems (such as user attention to different types of social media notifications on Instagram, or jobseekers' attention to job posts in LinkedIn). For LFM, we assume that the observed data is captured by the classical finite-dimensional Fisher market equilibrium, and its steady-state behavior is modeled by a continuous limit Fisher market. The second type of equilibrium we study, FPPE, arises from internet advertising applications, where advertisers are constrained by budgets and advertising opportunities are sold via first-price auctions. Pacing is a prevalent approach for managing advertiser budgets, where the platform assigns each advertiser an autobidder that controls expenditure by adaptively shading bids. For platforms that use pacing-based methods to smooth out the spending of advertisers, FPPE provides a steady-state description of the outcome of pacing-based markets.In Chapter 1 we develop a theory of statistical inference for LFMs and FPPE. In Chapter 2 we investigate theoretically sound bootstrap approaches for LFM and FPPE. In Chapter 3 we apply the theory to reduce interference bias in parallel A/B tests on ad auction platforms.
■590 ▼aSchool code: 0054.
■650 4▼aMathematics
■650 4▼aInformation science
■653 ▼aLinear Fisher market
■653 ▼aFirst-price pacing equilibrium
■653 ▼aStatistical inference
■653 ▼aCompetitive market effects
■690 ▼a0796
■690 ▼a0338
■690 ▼a0723
■690 ▼a0405
■71020▼aColumbia University▼bOperations Research.
■7730 ▼tDissertations Abstracts International▼g86-12A.
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357399▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
