On the possibilistic approach to linear regression models involving uncertain, indeterminate or interval data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10159427" target="_blank" >RIV/00216208:11320/13:10159427 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61384399:31140/13:00041479

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ins.2013.04.035" target="_blank" >http://dx.doi.org/10.1016/j.ins.2013.04.035</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ins.2013.04.035" target="_blank" >10.1016/j.ins.2013.04.035</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the possibilistic approach to linear regression models involving uncertain, indeterminate or interval data

Popis výsledku v původním jazyce

We consider linear regression models where both input data (the observations of independent variables) and output data (the observations of the dependent variable) are affected by loss of information caused by uncertainty, indeterminacy, rounding or censoring. Instead of real-valued (crisp) data, only intervals are available. We study a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. Investigation of the OLS-set allows us to quantify whether the replacement of real-valued (crisp) data by interval values can have a significant impact on our knowledge of the value of the OLS estimator. We show that in the general case, very elementary questions about properties of the OLS-set are computationally intractable (assuming P - NP). We also focus on restricted versions of the general interval linear regression model to the crisp input case. Taking the advantage of the fact that in the crisp input - interval output model the OLS-set is a zon

Název v anglickém jazyce

On the possibilistic approach to linear regression models involving uncertain, indeterminate or interval data

Popis výsledku anglicky

We consider linear regression models where both input data (the observations of independent variables) and output data (the observations of the dependent variable) are affected by loss of information caused by uncertainty, indeterminacy, rounding or censoring. Instead of real-valued (crisp) data, only intervals are available. We study a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. Investigation of the OLS-set allows us to quantify whether the replacement of real-valued (crisp) data by interval values can have a significant impact on our knowledge of the value of the OLS estimator. We show that in the general case, very elementary questions about properties of the OLS-set are computationally intractable (assuming P - NP). We also focus on restricted versions of the general interval linear regression model to the crisp input case. Taking the advantage of the fact that in the crisp input - interval output model the OLS-set is a zon

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

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

2013

Kód důvěrnosti údajů

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

Název periodika

Information Sciences

ISSN

0020-0255

e-ISSN

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Svazek periodika

244

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

26-47

Kód UT WoS článku

000321479800002

EID výsledku v databázi Scopus

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