Changepoint Detection by the Quantile LASSO Method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10419907" target="_blank" >RIV/00216208:11320/19:10419907 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=0tRrkyjza7" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=0tRrkyjza7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42519-019-0078-z" target="_blank" >10.1007/s42519-019-0078-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Changepoint Detection by the Quantile LASSO Method

Popis výsledku v původním jazyce

A simultaneous changepoint detection and estimation in a piece-wise constant model is a common task in modern statistics. If, in addition, the whole estimation can be performed fully automatically in a single step without requiring any statistical tests or a posteriori methods, it also becomes a very interesting but challenging idea. In this paper, we introduce the estimation method based on the quantile LASSO approach. Unlike standard LASSO approaches, our method does not rely on classical assumptions common for the model errors, sub-Gaussian or Normal distributions in particular. The quantile LASSO method can handle, for instance, outlying observations or heavy-tailed error distributions, and it provides, in general, a more complex insight into the data: any conditional quantile can be obtained rather than providing just the conditional mean. Under some reasonable assumptions, the number of changepoints is not underestimated with probability tenting to one. Moreover, if the number of changepoints is estimated correctly, the quantile LASSO changepoint estimators are consistent. Numerical simulations demonstrate the theoretical results, and they illustrate the empirical performance and the robust favor of the proposed quantile LASSO method. The real example is used to show a practical applicability of the proposed method.

Název v anglickém jazyce

Changepoint Detection by the Quantile LASSO Method

Popis výsledku anglicky

A simultaneous changepoint detection and estimation in a piece-wise constant model is a common task in modern statistics. If, in addition, the whole estimation can be performed fully automatically in a single step without requiring any statistical tests or a posteriori methods, it also becomes a very interesting but challenging idea. In this paper, we introduce the estimation method based on the quantile LASSO approach. Unlike standard LASSO approaches, our method does not rely on classical assumptions common for the model errors, sub-Gaussian or Normal distributions in particular. The quantile LASSO method can handle, for instance, outlying observations or heavy-tailed error distributions, and it provides, in general, a more complex insight into the data: any conditional quantile can be obtained rather than providing just the conditional mean. Under some reasonable assumptions, the number of changepoints is not underestimated with probability tenting to one. Moreover, if the number of changepoints is estimated correctly, the quantile LASSO changepoint estimators are consistent. Numerical simulations demonstrate the theoretical results, and they illustrate the empirical performance and the robust favor of the proposed quantile LASSO method. The real example is used to show a practical applicability of the proposed method.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Statistical Theory and Practice

ISSN

1559-8608

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

38

Strana od-do

11

Kód UT WoS článku

000511595900001

EID výsledku v databázi Scopus

2-s2.0-85076500007