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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

<a href="/en/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Opportunities for young researchers</a><br>

Continuities

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

Publication year

2014

Confidentiality

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

Name of the periodical

Electronic Journal of Applied Statistical Analysis

ISSN

2070-5948

e-ISSN

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Volume of the periodical

7

Issue of the periodical within the volume

1

Country of publishing house

IT - ITALY

Number of pages

21

Pages from-to

37-57

UT code for WoS article

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EID of the result in the Scopus database

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