Linear Trend Filtering via Adaptive LASSO

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10365801" target="_blank" >RIV/00216208:11320/17:10365801 - isvavai.cz</a>

Výsledek na webu

<a href="http://itise.ugr.es/proceedings/" target="_blank" >http://itise.ugr.es/proceedings/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Linear Trend Filtering via Adaptive LASSO

Popis výsledku v původním jazyce

Piece-wise linear models are quite popular real applications because of their overall simplicity and straightforward interpretation. In addition, such models are quite flexible in terms of their ability to adapt to existing changes in the trend which usually models the underlying time dependent structure. In this paper we propose an innovative ap- proach to the linear trend filtering which is based on the sparsity princi- ple in atomic pursuit estimation via an adaptive LASSO approach. The proposed method is oracle consistent and the final estimate can be con- structed with the same time efficiency as an ordinary linear regression. Moreover, one can take a full advantage of many efficient algorithms used to fit standard LASSO problems. We present some theoretical properties and the finite sample performance is investigated using a comparative simulation study.

Název v anglickém jazyce

Linear Trend Filtering via Adaptive LASSO

Popis výsledku anglicky

Piece-wise linear models are quite popular real applications because of their overall simplicity and straightforward interpretation. In addition, such models are quite flexible in terms of their ability to adapt to existing changes in the trend which usually models the underlying time dependent structure. In this paper we propose an innovative ap- proach to the linear trend filtering which is based on the sparsity princi- ple in atomic pursuit estimation via an adaptive LASSO approach. The proposed method is oracle consistent and the final estimate can be con- structed with the same time efficiency as an ordinary linear regression. Moreover, one can take a full advantage of many efficient algorithms used to fit standard LASSO problems. We present some theoretical properties and the finite sample performance is investigated using a comparative simulation study.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings ITISE 2017

ISBN

978-84-17293-01-7

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

12

Strana od-do

524-535

Název nakladatele

Godel Impresiones Digitales S.L.

Místo vydání

Španělsko

Místo konání akce

Granada

Datum konání akce

18. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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