Řídké odhady parametrů v přeparametrizovaných modelech časových řad

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F06%3A00017505" target="_blank" >RIV/00216224:14560/06:00017505 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Sparse Parameter Estimation in Overcomplete Time Series Models

Popis výsledku v původním jazyce

We suggest a new approach to parameter estimation in time series models with large number of parameters. We use a modified version of the Basis Pursuit Algorithm (BPA) by Chen et al [SIAM Review 43 (2001), No. 1] to verify its applicability to times series modeling. For simplicity we restrict to ARIMA models of univariate stationary time series. After having accomplished and analyzed a lot of numerical simulations we can draw the following conclusions: (1) We were able to reliably identify nearly zero parameters in the model allowing us to reduce the originally badly conditioned overparametrized model. Among others we need not take care about model orders the fixing of which is a common preliminary step used by standard techniques. For short time series paths (100 or less samples) the sparse parameter estimates provide more precise predictions compared with those based on standard maximum likelihood estimators from MATLAB's System Identification Toolbox (IDENT). For longer paths (500 o

Název v anglickém jazyce

Sparse Parameter Estimation in Overcomplete Time Series Models

Popis výsledku anglicky

We suggest a new approach to parameter estimation in time series models with large number of parameters. We use a modified version of the Basis Pursuit Algorithm (BPA) by Chen et al [SIAM Review 43 (2001), No. 1] to verify its applicability to times series modeling. For simplicity we restrict to ARIMA models of univariate stationary time series. After having accomplished and analyzed a lot of numerical simulations we can draw the following conclusions: (1) We were able to reliably identify nearly zero parameters in the model allowing us to reduce the originally badly conditioned overparametrized model. Among others we need not take care about model orders the fixing of which is a common preliminary step used by standard techniques. For short time series paths (100 or less samples) the sparse parameter estimates provide more precise predictions compared with those based on standard maximum likelihood estimators from MATLAB's System Identification Toolbox (IDENT). For longer paths (500 o

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2006

Kód důvěrnosti údajů

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

Název periodika

Austrian Journal of Statistics

ISSN

1026-597X

e-ISSN

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Svazek periodika

35/2006

Číslo periodika v rámci svazku

2&3

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

8

Strana od-do

371-378

Kód UT WoS článku

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EID výsledku v databázi Scopus

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