(A) study on improvement of high-dimensional function-based additive model = 고차원 함수 기반 가법 모형의 개선에 관한 연구

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In this paper, we introduce a Sparse Partially Linear Additive Models(SPLAM) to analyze a high-dimensional data. This is a model useful for variable selection and for checking if linearity of an independent variable is appropriate for explaining the dependent variable in an additive Model. We also introduce a method of bias reduction in parameter estimation by the Lasso. We introduce a new method of applying this method to the SPLAM to reduce the bias of parameter estimation in the SPLAM. To compare these two models, we use 3 loss functions for each data set. It is shown through experiments that the new method results in a decrease in error rate over the SPLAM.
Advisors
Kim, Sung-Horesearcher김성호researcher
Description
한국과학기술원 :수리과학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 수리과학과, 2019.2,[iii, 15 p. :]

Keywords

Sparse Partially Linear Additive Models(SPLAM)▼alasso▼abias▼aloss function; 희소 부분적 선형 가법 모형▼aLasso▼a편향▼a손실함수

URI
http://hdl.handle.net/10203/266406
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843277&flag=dissertation
Appears in Collection
MA-Theses_Master(석사논문)
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