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Statistical Learning and Decision Making for Spatio-Temporal Data
Statistical Learning and Decision Making for Spatio-Temporal Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105536
- ISBN
- 9798263390440
- DDC
- 790
- 저자명
- Zhu, Shixiang.
- 서명/저자
- Statistical Learning and Decision Making for Spatio-Temporal Data
- 발행사항
- [Sl] : Georgia Institute of Technology, 2022
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2022
- 형태사항
- 236 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: A.
- 주기사항
- Advisor: Xie, Yao.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2022.
- 초록/해제
- 요약Spatio-temporal data modeling and sequential decision analytics are a growing area of research, with an enormous amount of modern spatio-temporal data being consistently collected from the real world. These data include power outages, police 911 calls, healthcare records, credit card transactions, social media posts, etc. Understanding the intricate spatio-temporal dynamics behind these data requires the next generation of mathematical and statistical algorithms based on quantitative models of human and physical dynamics. This thesis presents the recent developments in this area with methodological advances and various real-world applications. We develop new theoretical and algorithmic techniques for capturing the dynamics of real-world spatio-temporal data by combining cutting-edge machine learning and classical statistical models. We also formulate the sequential decision-making processes as different optimization problems in a data driven manner, suggesting better decisions by taking advantage of the historical knowledge. Last but not least, we investigate a wide array of real-world spatio-temporal datasets using our proposed methods. The results demonstrate the value of spatio-temporal analytics in understanding computational, physical, and social systems.
- 일반주제명
- Design optimization
- 일반주제명
- Criminal statistics
- 일반주제명
- Redistricting
- 일반주제명
- Fourier transforms
- 일반주제명
- Neural networks
- 일반주제명
- Probability
- 일반주제명
- Robbery
- 일반주제명
- Keywords
- 일반주제명
- Burglary
- 일반주제명
- Fraud
- 일반주제명
- COVID-19
- 일반주제명
- Criminology
- 일반주제명
- Mathematics
- 기본자료저록
- Dissertations Abstracts International. 87-05A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798263390440
■035 ▼a(MiAaPQ)AAI32314824
■035 ▼a(MiAaPQ)GeorgiaTech66565
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a790
■1001 ▼aZhu, Shixiang.
■24510▼aStatistical Learning and Decision Making for Spatio-Temporal Data
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2022
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2022
■300 ▼a236 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: A.
■500 ▼aAdvisor: Xie, Yao.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2022.
■520 ▼aSpatio-temporal data modeling and sequential decision analytics are a growing area of research, with an enormous amount of modern spatio-temporal data being consistently collected from the real world. These data include power outages, police 911 calls, healthcare records, credit card transactions, social media posts, etc. Understanding the intricate spatio-temporal dynamics behind these data requires the next generation of mathematical and statistical algorithms based on quantitative models of human and physical dynamics. This thesis presents the recent developments in this area with methodological advances and various real-world applications. We develop new theoretical and algorithmic techniques for capturing the dynamics of real-world spatio-temporal data by combining cutting-edge machine learning and classical statistical models. We also formulate the sequential decision-making processes as different optimization problems in a data driven manner, suggesting better decisions by taking advantage of the historical knowledge. Last but not least, we investigate a wide array of real-world spatio-temporal datasets using our proposed methods. The results demonstrate the value of spatio-temporal analytics in understanding computational, physical, and social systems.
■590 ▼aSchool code: 0078.
■650 4▼aDesign optimization
■650 4▼aCriminal statistics
■650 4▼aRedistricting
■650 4▼aFourier transforms
■650 4▼aNeural networks
■650 4▼aProbability
■650 4▼aRobbery
■650 4▼aKeywords
■650 4▼aBurglary
■650 4▼aFraud
■650 4▼aCOVID-19
■650 4▼aCriminology
■650 4▼aMathematics
■690 ▼a0800
■690 ▼a0627
■690 ▼a0405
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05A.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2022
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360496▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
