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Tuning Parameter Selection for the Fused Lasso Signal Approximator

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Authors
손원
Advisor
임요한
Major
자연과학대학 통계학과
Issue Date
2017-02
Publisher
서울대학교 대학원
Keywords
multiple changepointsfused lasso signal approximatortuning parameter selectionfalse discovery rate
Description
학위논문 (박사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 임요한.
Abstract
In this thesis, we propose a two-stage tuning parameter selection procedure for the fused lasso signal approximator (FLSA).
The FLSA can be used to recover piecewise constant mean vectors by penalizing the differences of the neighboring mean values.
Like other regularized methods, choice of the tuning parameters affects the quality of model selection and estimation.
We find a theoretically optimal fusion parameter for identifying the true changepoints.
As a practical fusion parameter selection procedure, we propose a generalized information criteria (GIC) with theoretical justification.
For any preliminary test statistic which has a piecewise constant mean structure, the adaptive fusion estimator built on the preliminary test statistic gives lower false discovery rate (FDR).
Language
English
URI
https://hdl.handle.net/10371/121165
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Ph.D. / Sc.D._통계학과)
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