Central limit theorem for the spiked eigenvalues of separable sample covariance matrices
This thesis is concerned about the central limit theorems for the spiked eigenvalues of separable sample covariance matrices and their applications. The first problem is to test a p-dimensional time series model with unit root. We establish both the convergence in probability and the asymptot...
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| 主要作者: | |
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| 其他作者: | |
| 格式: | Theses and Dissertations |
| 語言: | English |
| 出版: |
2017
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| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10356/70338 |
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| 總結: | This thesis is concerned about the central limit theorems for the spiked
eigenvalues of separable sample covariance matrices and their applications.
The first problem is to test a p-dimensional time series model with unit root. We establish both the convergence in probability
and the asymptotic joint distribution of the first k largest eigenvalues of separable sample covariance matrices. Then we
give two new unit root tests for high-dimensional time series as applications.
We also provide some simulation results about the two tests.
Then we extend our theoretical results to the more general case. We
study the separable sample covariance matrix with two different
kinds of population covariance matrices and each of them has some extremely large eigenvalues. We
prove the central limit theorems of the largest eigenvalues for the two cases
and give two examples in time series data. |
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