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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sg-ntu-dr.10356-703382023-02-28T23:39:25Z Central limit theorem for the spiked eigenvalues of separable sample covariance matrices Zhang, Bo Pan Guangming School of Physical and Mathematical Sciences DRNTU::Science::Mathematics::Statistics 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. Doctor of Philosophy (SPMS) 2017-04-20T08:05:37Z 2017-04-20T08:05:37Z 2017 Thesis Zhang, B. (2017). Central limit theorem for the spiked eigenvalues of separable sample covariance matrices. Doctoral thesis, Nanyang Technological University, Singapore. http://hdl.handle.net/10356/70338 10.32657/10356/70338 en 131 p. application/pdf |
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DRNTU::Science::Mathematics::Statistics Zhang, Bo Central limit theorem for the spiked eigenvalues of separable sample covariance matrices |
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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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Pan Guangming |
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Pan Guangming Zhang, Bo |
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Theses and Dissertations |
author |
Zhang, Bo |
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Zhang, Bo |
title |
Central limit theorem for the spiked eigenvalues of separable sample covariance matrices |
title_short |
Central limit theorem for the spiked eigenvalues of separable sample covariance matrices |
title_full |
Central limit theorem for the spiked eigenvalues of separable sample covariance matrices |
title_fullStr |
Central limit theorem for the spiked eigenvalues of separable sample covariance matrices |
title_full_unstemmed |
Central limit theorem for the spiked eigenvalues of separable sample covariance matrices |
title_sort |
central limit theorem for the spiked eigenvalues of separable sample covariance matrices |
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2017 |
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http://hdl.handle.net/10356/70338 |
_version_ |
1759854519473143808 |