The Evaluation of a Concomitant Variable Behaviour in a Mixture of Regression Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F17%3A73584957" target="_blank" >RIV/61989592:15310/17:73584957 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.czso.cz/documents/10180/45606529/32019717q4061.pdf/416877ab-2812-4622-a16c-f5a5a75bd691?version=1.0" target="_blank" >https://www.czso.cz/documents/10180/45606529/32019717q4061.pdf/416877ab-2812-4622-a16c-f5a5a75bd691?version=1.0</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The Evaluation of a Concomitant Variable Behaviour in a Mixture of Regression Models

Popis výsledku v původním jazyce

Finite mixture of regression models are a popular technique for modelling the unobserved heterogeneity that occurs in the population. This method acquires parameters estimates by modelling a mixture conditional distribution of the response given explanatory variables. Since this optimization problem appears to be too computationally demanding, the expectation-maximization (EM) algorithm, an iterative algorithm for computing maximum likelihood estimates from incomplete data, is used in practice. In order to specify different components with higher accuracy and to improve regression parameter estimates and predictions the use of concomitant variables has been proposed. Based on a simulation study, performance and obvious advantages of concomitant variables are presented. A practical choice of appropriate concomitant variable and the effect of predictors&apos; domains on the estimation are discussed as well.

Název v anglickém jazyce

The Evaluation of a Concomitant Variable Behaviour in a Mixture of Regression Models

Popis výsledku anglicky

Finite mixture of regression models are a popular technique for modelling the unobserved heterogeneity that occurs in the population. This method acquires parameters estimates by modelling a mixture conditional distribution of the response given explanatory variables. Since this optimization problem appears to be too computationally demanding, the expectation-maximization (EM) algorithm, an iterative algorithm for computing maximum likelihood estimates from incomplete data, is used in practice. In order to specify different components with higher accuracy and to improve regression parameter estimates and predictions the use of concomitant variables has been proposed. Based on a simulation study, performance and obvious advantages of concomitant variables are presented. A practical choice of appropriate concomitant variable and the effect of predictors&apos; domains on the estimation are discussed as well.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Statistika: Statistic and Economy Journal

ISSN

0322-788X

e-ISSN

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Svazek periodika

97, 2017

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

15

Strana od-do

61-75

Kód UT WoS článku

000419162000006

EID výsledku v databázi Scopus

2-s2.0-85039742186