Linear Trend Filtering via Adaptive LASSO

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10384379" target="_blank" >RIV/00216208:11320/18:10384379 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-96944-2" target="_blank" >http://dx.doi.org/10.1007/978-3-319-96944-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-96944-2" target="_blank" >10.1007/978-3-319-96944-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Linear Trend Filtering via Adaptive LASSO

Popis výsledku v původním jazyce

Linear trend filtering methods are popular due to their over- all simplicity - the model is linear in each segment and there are typi- cally only few segments considered. These segments are defined by unique points where the trend changes its direction - so called changepoints. In this paper we consider an innovative estimation approach for such mod- els. Our proposal is based on recent developments in the atomic pursuit techniques: we present an estimation algorithm based on the adaptive LASSO penalty and we introduce a fully data-driven method which can be effectively used to fit the continuous linear trend models. Some statis- tical properties are discussed and the empirical performance is compared with respect to other competitive LASSO based techniques.

Název v anglickém jazyce

Linear Trend Filtering via Adaptive LASSO

Popis výsledku anglicky

Linear trend filtering methods are popular due to their over- all simplicity - the model is linear in each segment and there are typi- cally only few segments considered. These segments are defined by unique points where the trend changes its direction - so called changepoints. In this paper we consider an innovative estimation approach for such mod- els. Our proposal is based on recent developments in the atomic pursuit techniques: we present an estimation algorithm based on the adaptive LASSO penalty and we introduce a fully data-driven method which can be effectively used to fit the continuous linear trend models. Some statis- tical properties are discussed and the empirical performance is compared with respect to other competitive LASSO based techniques.

Druh

C - Kapitola v odborné knize

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

2018

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Time Series Analysis and Forecasting

ISBN

978-3-319-96943-5

Počet stran výsledku

15

Strana od-do

1-15

Počet stran knihy

340

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Springer Nature Switzerland AG

Kód UT WoS kapitoly

—