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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • 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