Asymptotic and Bootstrap Tests for a Change in Autoregression Omitting Variability Estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00484127" target="_blank" >RIV/67985807:_____/18:00484127 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/18:10383236
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-96944-2_13" target="_blank" >http://dx.doi.org/10.1007/978-3-319-96944-2_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-96944-2_13" target="_blank" >10.1007/978-3-319-96944-2_13</a>
Alternative languages
Result language
angličtina
Original language name
Asymptotic and Bootstrap Tests for a Change in Autoregression Omitting Variability Estimation
Original language description
A sequence of time-ordered observations follows an autoregressive model of order one and its parameter is possibly subject to change at most once at some unknown time point. The aim is to test whether such an unknown change has occurred or not. A change-point method presented here rely on a ratio type test statistic based on the maxima of cumulative sums. The main advantage of the developed approach is that the variance of the observations neither has to be known nor estimated. Asymptotic distribution of the test statistic under the no-change null hypothesis is derived. Moreover, we prove the consistency of the test under the alternative. A bootstrap procedure is proposed in the way of a completely data-driven technique without any tuning parameters. The results are illustrated through a simulation study, which demonstrates the computational efficiency of the procedure. A practical application to real data is presented as well.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Time Series Analysis and Forecasting: Selected Contributions from ITISE 2017
ISBN
978-3-319-96943-5
ISSN
1431-1968
e-ISSN
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Number of pages
16
Pages from-to
187-202
Publisher name
Springer
Place of publication
Cham
Event location
Granada
Event date
Sep 18, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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