Data-adaptive Principal Component Analysis for High Dimensional Data
Among many well designed techniques for dimension reduction, the Principal Component Analysis (PCA) is one of the most popular and applicable methods. In this thesis, we address the challenges encountered when modelling and forecasting the high-dimensional data with PCA related methods in three problems. In Chapter 2, we propose a two-style factor model to improve the forecasting of high-dimensional time series. The model pursues two types of low-dimensional features for the original...[Show more]
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|Lingyu He Thesis 2020.pdf||Thesis Material||1.61 MB||Adobe PDF|
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