Change-point detection in a linear model by adaptive fused quantile method
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10419906" target="_blank" >RIV/00216208:11320/20:10419906 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZErJE_Vrm~" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZErJE_Vrm~</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/sjos.12412" target="_blank" >10.1111/sjos.12412</a>
Alternative languages
Result language
angličtina
Original language name
Change-point detection in a linear model by adaptive fused quantile method
Original language description
A novel approach to quantile estimation in multivariate linear regression models with change-points is proposed: the change-point detection and the model estimation are both performed automatically, by adopting either the quantile-fused penalty or the adaptive version of the quantile-fused penalty. These two methods combine the idea of the check function used for the quantile estimation and the L1 penalization principle known from the signal processing and, unlike some standard approaches, the presented methods go beyond typical assumptions usually required for the model errors, such as sub- Gaussian or normal distribution. They can effectively handle heavy-tailed random error distributions, and, in general, they offer a more complex view on the data as one can obtain any conditional quantile of the target distribution, not just the conditional mean. The consistency of detection is proved and proper convergence rates for the parameter estimates are derived. The empirical performance is investigated via an extensive comparative simulation study and practical utilization is demonstrated using a real data example.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scandinavian Journal of Statistics
ISSN
0303-6898
e-ISSN
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Volume of the periodical
47
Issue of the periodical within the volume
2
Country of publishing house
GB - UNITED KINGDOM
Number of pages
39
Pages from-to
425-463
UT code for WoS article
000538731300005
EID of the result in the Scopus database
2-s2.0-85076482428