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Nonparametric inference on nonstatio...

Zhang, Ting.

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Nonparametric inference on nonstationary time series.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nonparametric inference on nonstationary time series./
Author:	Zhang, Ting.
Description:	97 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-01(E), Section: B.
Contained By:	Dissertation Abstracts International74-01B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3526375
ISBN:	9781267602497

Nonparametric inference on nonstationary time series.
Zhang, Ting.

Nonparametric inference on nonstationary time series. - 97 p.

Source: Dissertation Abstracts International, Volume: 74-01(E), Section: B.

Thesis (Ph.D.)--The University of Chicago, 2012.

Nonparametric methods are model-free approaches that can be useful in assessing parametric and semiparametric models. The problem of testing parametric assumptions has been widely studied in the literature, but mainly for independent data. However, the later assumption can be easily violated in time series analysis where dependence is the rule rather than the exception. In this thesis, we consider the situation with locally stationary processes, a special class of nonstationary processes. We start with the problem of testing whether the mean trend of a locally stationary process falls into a certain parametric form. A central limit theorem for the integrated squared error is derived, and a simulation-assisted hypothesis testing procedure is proposed to improve the finite-sample performance. We demonstrate by simulation that ignoring the underlying dependence can lead to erroneous conclusions. The method is applied to assess the trend pattern of lifetime-maximum wind speeds of tropical cyclones and the central England temperature series. Its extension to high dimensional time series data and time-varying coefficient models are also considered.

ISBN: 9781267602497Subjects--Topical Terms:

517247
Statistics.

Nonparametric inference on nonstationary time series.
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020 $a 9781267602497
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100 1 $a Zhang, Ting. $3 1282232
245 1 0 $a Nonparametric inference on nonstationary time series.
300 $a 97 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-01(E), Section: B.
500 $a Advisers: Wei Biao Wu; Lars P. Hansen.
502 $a Thesis (Ph.D.)--The University of Chicago, 2012.
520 $a Nonparametric methods are model-free approaches that can be useful in assessing parametric and semiparametric models. The problem of testing parametric assumptions has been widely studied in the literature, but mainly for independent data. However, the later assumption can be easily violated in time series analysis where dependence is the rule rather than the exception. In this thesis, we consider the situation with locally stationary processes, a special class of nonstationary processes. We start with the problem of testing whether the mean trend of a locally stationary process falls into a certain parametric form. A central limit theorem for the integrated squared error is derived, and a simulation-assisted hypothesis testing procedure is proposed to improve the finite-sample performance. We demonstrate by simulation that ignoring the underlying dependence can lead to erroneous conclusions. The method is applied to assess the trend pattern of lifetime-maximum wind speeds of tropical cyclones and the central England temperature series. Its extension to high dimensional time series data and time-varying coefficient models are also considered.
590 $a School code: 0330.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a The University of Chicago. $b Statistics. $3 1673632
773 0 $t Dissertation Abstracts International $g 74-01B(E).
790 $a 0330
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3526375