Detection of similar successive groups in a model with diverging number of variable groups

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10419912" target="_blank" >RIV/00216208:11320/20:10419912 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uu5jxgJ1vB" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uu5jxgJ1vB</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/07474946.2020.1726687" target="_blank" >10.1080/07474946.2020.1726687</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of similar successive groups in a model with diverging number of variable groups

Popis výsledku v původním jazyce

In this article, a linear model with grouped explanatory variables is considered. The idea is to perform an automatic detection of different successive groups of the unknown coefficients under the assumption that the number of groups is of the same order as the sample size. The standard least squares loss function and the quantile loss function are both used together with the fused and adaptive fused penalty to simultaneously estimate and group the unknown parameters. The proper convergence rate is given for the obtained estimators and the upper bound for the number of different successive group is derived. A simulation study is used to compare the empirical performance of the proposed fused and adaptive fused estimators, and a real application on the air quality data demonstrates the practical applicability of the proposed methods.

Název v anglickém jazyce

Detection of similar successive groups in a model with diverging number of variable groups

Popis výsledku anglicky

In this article, a linear model with grouped explanatory variables is considered. The idea is to perform an automatic detection of different successive groups of the unknown coefficients under the assumption that the number of groups is of the same order as the sample size. The standard least squares loss function and the quantile loss function are both used together with the fused and adaptive fused penalty to simultaneously estimate and group the unknown parameters. The proper convergence rate is given for the obtained estimators and the upper bound for the number of different successive group is derived. A simulation study is used to compare the empirical performance of the proposed fused and adaptive fused estimators, and a real application on the air quality data demonstrates the practical applicability of the proposed methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GJ18-00522Y" target="_blank" >GJ18-00522Y: Pokročilé Ekonometrické Modely pro Oceňování Opcí – AdEMOP</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Sequential Analysis

ISSN

0747-4946

e-ISSN

—

Svazek periodika

39

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

92-114

Kód UT WoS článku

000532639000006

EID výsledku v databázi Scopus

2-s2.0-85084832257