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

黃學涵(Hsueh-Han Huang)

指導教授 ： 銀慶剛

國立臺灣大學/理學院/應用數學科學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201700925

摘要

無資料

關鍵字

高維度 ； 測量誤差 ； 正交化貪婪演算法 ； 稀疏性 ； 時間序列

並列摘要

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.

並列關鍵字

High-dimensional ； measurement error ； OGA ； sparsity ； time series

參考文獻

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Alexandre Belloni, Mathieu Rosenbaum and Alexandre B.Tsybakov. (2016). Linear and Conic Programming Estimators in High-Dimensional Errors-in variables Models. https://arxiv.org/abs/1408.0241.

Ching-Kang Ing and Tze Leung Lai (2011). A stepwise regression method and consistent model selection for high-dimensional sparse linear models. Statist.Sinica,1473-1513.

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