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Finite mixture models and model-based clustering

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Authors
김경민
Advisor
임요한
Major
자연과학대학 통계학과
Issue Date
2017-02
Publisher
서울대학교 대학원
Keywords
EM algorithmmodel selectionvariable selectiondiagnosticstwo-dimensional gel electrophoresis datamagnitude magnetic resonance images.
Description
학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 임요한.
Abstract
Abstract


Kyoung-Min Kim
The Department of Statistics
The Graduate School
Seoul National University


Finite mixture models aims at identifying clusters of individuals who show

similar patterns. The method is having been used in a variety of fields,

especially in medicine to explain the idea of heterogeneity of treatment

effects on population. The number of mixture components is typically not

known and has to be chosen.

To solve this problem, EM algorithm-based approaches is

considered. We will review details of mixture models and model-based

clustering. Furthermore, we will provide an overview of several challenges

that have been only partially resolved.




Note: Writing this paper, I mainly refer to "Finite mixture models and model-based clustering"(Melnykov, 2010)


Keyword : EM algorithm, model selection, variable selection, diagnostics, two-dimensional gel electrophoresis data, magnitude magnetic resonance images.


Student Number : 2014-20298
Language
Korean
URI
https://hdl.handle.net/10371/131325
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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