Estimating the Model with Fixed and Random Effects by a Robust Method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11230%2F15%3A10312391" target="_blank" >RIV/00216208:11230/15:10312391 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11009-014-9432-5" target="_blank" >http://dx.doi.org/10.1007/s11009-014-9432-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11009-014-9432-5" target="_blank" >10.1007/s11009-014-9432-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimating the Model with Fixed and Random Effects by a Robust Method

Popis výsledku v původním jazyce

Regression model with fixed and random effects estimated by modified versions of the Ordinary Least Squares (OLS) is a standard tool of panel data analysis. However, it is vulnerable to the bad effects of influential observations (contamination and/or atypical observations). The paper offers robustified versions of the classical methods for this framework. The robustification is carried out by the same idea which was employed when robustifying OLS, it is the idea of weighting down the large order statistics of squared residuals. In contrast to the approach based on the M-estimators this approach does not need the studentization of residuals to reach the scale- and regression-equivariance of estimator in question. Moreover, such approach is not vulnerable with respect the inliers. The numerical study reveals the reliability of the respective algorithm. The results of this study were collected in a file which is possible to find on web, address is given below. Patterns of these results w

Název v anglickém jazyce

Estimating the Model with Fixed and Random Effects by a Robust Method

Popis výsledku anglicky

Regression model with fixed and random effects estimated by modified versions of the Ordinary Least Squares (OLS) is a standard tool of panel data analysis. However, it is vulnerable to the bad effects of influential observations (contamination and/or atypical observations). The paper offers robustified versions of the classical methods for this framework. The robustification is carried out by the same idea which was employed when robustifying OLS, it is the idea of weighting down the large order statistics of squared residuals. In contrast to the approach based on the M-estimators this approach does not need the studentization of residuals to reach the scale- and regression-equivariance of estimator in question. Moreover, such approach is not vulnerable with respect the inliers. The numerical study reveals the reliability of the respective algorithm. The results of this study were collected in a file which is possible to find on web, address is given below. Patterns of these results w

Druh

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

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

<a href="/cs/project/GA13-01930S" target="_blank" >GA13-01930S: Robustní procedury pro nestandardní situace, jejich diagnostika a implementace</a><br>

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Methodology and Computing in Applied Probability

ISSN

1387-5841

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

999-1014

Kód UT WoS článku

000365182300011

EID výsledku v databázi Scopus

2-s2.0-84947488277