國立虎尾科技大學 |

Estimation and Inference for High Dimensional Time Series.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Estimation and Inference for High Dimensional Time Series./
作者:	Zhang, Danna.
面頁冊數:	1 online resource (125 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Contained By:	Dissertation Abstracts International79-02B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355233957

Estimation and Inference for High Dimensional Time Series.
Zhang, Danna.

Estimation and Inference for High Dimensional Time Series. - 1 online resource (125 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

There is a well-developed asymptotic theory for sample means and sample second-order statistics of low dimensional stationary processes. However, many important problems on their asymptotic behaviors are still unanswered for time series which can be high-dimensional, nonstationary and non-Gaussian.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355233957Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Estimation and Inference for High Dimensional Time Series.
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100 1 $a Zhang, Danna. $3 1182967
245 1 0 $a Estimation and Inference for High Dimensional Time Series.
264 0 $c 2017
300 $a 1 online resource (125 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
500 $a Adviser: Wei Biao Wu.
502 $a Thesis (Ph.D.) $c The University of Chicago $d 2017.
504 $a Includes bibliographical references
520 $a There is a well-developed asymptotic theory for sample means and sample second-order statistics of low dimensional stationary processes. However, many important problems on their asymptotic behaviors are still unanswered for time series which can be high-dimensional, nonstationary and non-Gaussian.
520 $a This thesis concerns the estimation and inference of high-dimensional time series under the framework of functional dependence measure. We first consider the problem of approximating sums of high dimensional stationary time series by Gaussian vectors. We also consider an estimator for long-run covariance matrices and study its convergence properties. Our results allow constructing simultaneous confidence intervals for mean vectors of high-dimensional time series with asymptotically correct coverage probabilities. As an application, we can do simultaneous inferences for covariance matrices of high-dimensional stationary time series. We also propose a Kolmogorov-Smirnov type statistic for testing distributions of high-dimensional time series.
520 $a This thesis also presents a systematic asymptotic theory for the estimates of time-varying second-order statistics for a general class of high-dimensional nonstationary processes. In particular, we investigate the estimation of time-varying autocovariance matrix functions, spectral density matrices and coherence matrices for high-dimensional locally stationary processes. Besides, we use the constrained &ell; 1 minimization approach to estimate the inverse of the spectral density matrix which can be used to identify the graphical structure for high-dimensional locally stationary processes. We derive the convergence rates of the estimates which depend on the sample size, the dimension, the moment condition and the dependence of the underlying processes.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Chicago. $b Statistics. $3 1180881
773 0 $t Dissertation Abstracts International $g 79-02B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10601289 $z click for full text (PQDT)