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Tuning Parameter Selection for the Fused Lasso Signal Approximator
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- Authors
- Advisor
- 임요한
- Major
- 자연과학대학 통계학과
- Issue Date
- 2017-02
- Publisher
- 서울대학교 대학원
- Keywords
- multiple changepoints ; fused lasso signal approximator ; tuning parameter selection ; false 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
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