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  • 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.
  • 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. (en)
Title
  • A modified minimum divergence estimator: some preliminary results for the Rasch model
  • A modified minimum divergence estimator: some preliminary results for the Rasch model (en)
skos:prefLabel
  • A modified minimum divergence estimator: some preliminary results for the Rasch model
  • A modified minimum divergence estimator: some preliminary results for the Rasch model (en)
skos:notation
  • RIV/61989100:27510/14:86090960!RIV15-MSM-27510___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(EE2.3.30.0016)
http://linked.open...iv/cisloPeriodika
  • 1
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Lando, Tommaso
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 846
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27510/14:86090960
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • bias; minimum divergence estimate; MLE; Rasch model (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • IT - Italská republika
http://linked.open...ontrolniKodProRIV
  • [ADC907ECC10E]
http://linked.open...i/riv/nazevZdroje
  • Electronic Journal of Applied Statistical Analysis
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 7
http://linked.open...iv/tvurceVysledku
  • Bertoli-Barsotti, Lucio
  • Lando, Tommaso
issn
  • 2070-5948
number of pages
http://bibframe.org/vocab/doi
  • 10.1285/i20705948v7n1p37
http://localhost/t...ganizacniJednotka
  • 27510
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