Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2533
Title: | 高維度時間序列並帶有測量誤差模型之模型選擇 Model Selection for High-Dimensional Time Series Models with Measurement Errors |
Authors: | Hsueh-Han Huang 黃學涵 |
Advisor: | 銀慶剛 |
Keyword: | 高維度,測量誤差,正交化貪婪演算法,稀疏性,時間序列, High-dimensional,measurement error,OGA,sparsity,time series, |
Publication Year : | 2017 |
Degree: | 碩士 |
Abstract: | We use a fast stepwise regression method, called orthogonal greedy algorithm (OGA) to select variables for high-dimensional time series model with measurement errors. Under a weak sparsity condition, we derive a convergence rate of OGA, which is expressed in terms of the number of iterations, the sample size and the order of the moment imposed on the error process. Under a strong sparsity condition, we develop a consistent model selection procedure using OGA and a high-dimensional information criterion. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2533 |
DOI: | 10.6342/NTU201700925 |
Fulltext Rights: | 同意授權(全球公開) |
Appears in Collections: | 應用數學科學研究所 |
Files in This Item:
File | Size | Format | |
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ntu-106-1.pdf | 912.26 kB | Adobe PDF | View/Open |
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