A modified minimum divergence estimator: some preliminary results for the Rasch model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F14%3A86090960" target="_blank" >RIV/61989100:27510/14:86090960 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1285/i20705948v7n1p37" target="_blank" >http://dx.doi.org/10.1285/i20705948v7n1p37</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1285/i20705948v7n1p37" target="_blank" >10.1285/i20705948v7n1p37</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A modified minimum divergence estimator: some preliminary results for the Rasch model

Popis výsledku v původním jazyce

Since its introduction, the joint maximum likelihood (JML) has been widely used as an estimation method for Rasch measurement models. As is well known, when the JML method is used, all item and person parameters are regarded as unknowns to be estimated.In this paper we focus on some drawbacks of the JML for the Rasch model: viz. i) the occasional non-existence of estimates, and ii) the bias of item parameter estimates. We propose a new estimation method which is based on the Minimum Divergence Estimation approach and consists in appropriately modifying the empirical distribution function. We provide empirical evidence that this method can solve the problem of the non-existence of the estimates and, at the same time, can reduce the bias of item parameter estimates compared to those obtained with both traditional JML estimation and the (k-1)=k correction factor (where k is the number of items) commonly applied in JML software.

Název v anglickém jazyce

A modified minimum divergence estimator: some preliminary results for the Rasch model

Popis výsledku anglicky

Since its introduction, the joint maximum likelihood (JML) has been widely used as an estimation method for Rasch measurement models. As is well known, when the JML method is used, all item and person parameters are regarded as unknowns to be estimated.In this paper we focus on some drawbacks of the JML for the Rasch model: viz. i) the occasional non-existence of estimates, and ii) the bias of item parameter estimates. We propose a new estimation method which is based on the Minimum Divergence Estimation approach and consists in appropriately modifying the empirical distribution function. We provide empirical evidence that this method can solve the problem of the non-existence of the estimates and, at the same time, can reduce the bias of item parameter estimates compared to those obtained with both traditional JML estimation and the (k-1)=k correction factor (where k is the number of items) commonly applied in JML software.

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

<a href="/cs/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Příležitost pro mladé výzkumníky</a><br>

Návaznosti

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

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Electronic Journal of Applied Statistical Analysis

ISSN

2070-5948

e-ISSN

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Svazek periodika

7

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

21

Strana od-do

37-57

Kód UT WoS článku

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EID výsledku v databázi Scopus

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