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Bootstrap prediction intervals for t...

Pan, Li.

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Bootstrap prediction intervals for time series.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bootstrap prediction intervals for time series./
Author:	Pan, Li.
Description:	153 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
Contained By:	Dissertation Abstracts International75-03B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3602962
ISBN:	9781303566622

Bootstrap prediction intervals for time series.
Pan, Li.

Bootstrap prediction intervals for time series. - 153 p.

Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2013.

We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, nonparametric autoregressions and Markov processes. Several forward and backward bootstrap methods using predictive residuals and fitted residuals are introduced and applied to those time series. We describe exact algorithms for these different models and show that the bootstrap intervals properly estimate the distribution of the future values. In simulations using standard time series models, we compare the prediction intervals of different methods with regards to coverage level and length of interval.

ISBN: 9781303566622Subjects--Topical Terms:

517247
Statistics.

Bootstrap prediction intervals for time series.
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020 $a 9781303566622
035 $a (MiAaPQ)AAI3602962
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100 1 $a Pan, Li. $3 1294820
245 1 0 $a Bootstrap prediction intervals for time series.
300 $a 153 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
500 $a Adviser: Dimitris Politis.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2013.
520 $a We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, nonparametric autoregressions and Markov processes. Several forward and backward bootstrap methods using predictive residuals and fitted residuals are introduced and applied to those time series. We describe exact algorithms for these different models and show that the bootstrap intervals properly estimate the distribution of the future values. In simulations using standard time series models, we compare the prediction intervals of different methods with regards to coverage level and length of interval.
590 $a School code: 0033.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a University of California, San Diego. $b Mathematics. $3 1022364
773 0 $t Dissertation Abstracts International $g 75-03B(E).
790 $a 0033
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3602962