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Fusion Learning of dependent studies by confidence distribution (CD)
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Li, Chengrui. Fusion Learning of dependent studies by confidence distribution (CD). Retrieved from https://doi.org/doi:10.7282/T35M68K2

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TitleFusion Learning of dependent studies by confidence distribution (CD)
NameLi, Chengrui (author); Xie, Minge (chair); Liu, Regina (internal member); Hung, Ying (internal member); Cheng, Jerry (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2017
Other Date2017-05 (degree)
SubjectStatistics and Biostatistics
Extent1 online resource (x, 75 p. : ill.)
DescriptionThis dissertation focuses on developing efficient Fusion Learning methodologies for combining information from non-independent sources using {it confidence distribution} (CD). The sources hereby are broadly construed as different pieces of information extracted from possibly correlated datasets. This situation typically arises when multiple inferences are performed over different times, locations or experiment settings due to computational and statistical considerations, which encompasses a wide range of scientific and engineering applications (e.g. seismic monitoring and detection, computer experiments). In this dissertation, we develop a general framework to effectively and efficiently combine these correlated information using CD, and furthermore, explore the advantages of this framework in different problems that are of theoretical and practical interests.
NotePh.D.
NoteIncludes bibliographical references
Noteby Chengrui Li
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T35M68K2
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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