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Sample Size Determination for Subsampling in the Analysis of Big Data, Multiplicative Models for Confidence Intervals and Free-Knot Changepoint Models
Sample Size Determination for Subsampling in the Analysis of Big Data, Multiplicative Models for Confidence Intervals and Free-Knot Changepoint Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152739
- ISBN
- 9798384343899
- DDC
- 510
- 저자명
- Zhang, Sheng.
- 서명/저자
- Sample Size Determination for Subsampling in the Analysis of Big Data, Multiplicative Models for Confidence Intervals and Free-Knot Changepoint Models
- 발행사항
- [Sl] : Purdue University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 97 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Peng, Hanxiang.
- 학위논문주기
- Thesis (Ph.D.)--Purdue University, 2024.
- 초록/해제
- 요약Motivated by subsampling in the analysis of Big Data and by data-splitting in machine learning, sample size determination for multidimensional parameters is presented in the first part.In the second part, we propose a novel approach to the construction of confidence intervals based on improved concentration inequalities. We provide the missing factor for the tail probability of a random variable which generalizes Talagrand's (1995) result of the missing factor in Hoeffding's inequalities. We give the procedure for constructing confidence intervals and illustrate it with simulations.In the third part, we study irregular change-point models using free-knot splines. The consistency and asymptotic normality of the least squares estimators are proved for the irregular models in which the linear spline is not differentiable. Simulations are carried out to explore the numerical properties of the proposed models. The results are used to analyze the US Covid-19 data.
- 일반주제명
- Sample size
- 일반주제명
- Normal distribution
- 일반주제명
- Binomial distribution
- 일반주제명
- Software utilities
- 일반주제명
- Probability
- 일반주제명
- Eigenvalues
- 일반주제명
- Central limit theorem
- 일반주제명
- Parameter estimation
- 일반주제명
- Statistics
- 기타저자
- Purdue University.
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384343899
■035 ▼a(MiAaPQ)AAI31496308
■035 ▼a(MiAaPQ)Purdue25724451
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a510
■1001 ▼aZhang, Sheng.
■24510▼aSample Size Determination for Subsampling in the Analysis of Big Data, Multiplicative Models for Confidence Intervals and Free-Knot Changepoint Models
■260 ▼a[Sl]▼bPurdue University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a97 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Peng, Hanxiang.
■5021 ▼aThesis (Ph.D.)--Purdue University, 2024.
■520 ▼aMotivated by subsampling in the analysis of Big Data and by data-splitting in machine learning, sample size determination for multidimensional parameters is presented in the first part.In the second part, we propose a novel approach to the construction of confidence intervals based on improved concentration inequalities. We provide the missing factor for the tail probability of a random variable which generalizes Talagrand's (1995) result of the missing factor in Hoeffding's inequalities. We give the procedure for constructing confidence intervals and illustrate it with simulations.In the third part, we study irregular change-point models using free-knot splines. The consistency and asymptotic normality of the least squares estimators are proved for the irregular models in which the linear spline is not differentiable. Simulations are carried out to explore the numerical properties of the proposed models. The results are used to analyze the US Covid-19 data.
■590 ▼aSchool code: 0183.
■650 4▼aSample size
■650 4▼aNormal distribution
■650 4▼aBinomial distribution
■650 4▼aSoftware utilities
■650 4▼aProbability
■650 4▼aEigenvalues
■650 4▼aCentral limit theorem
■650 4▼aParameter estimation
■650 4▼aStatistics
■690 ▼a0800
■690 ▼a0463
■71020▼aPurdue University.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0183
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163676▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
