How to fit models of recognition memory data using maximum likelihood.

The aim of this paper is to provide an introductory tutorial to how to fit different models of recognition memory using maximum likelihood estimation. It is in four main parts. The first part describes how recognition memory data is collected and analysed. The second part introduces four current mod...

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Autor principal:	John C. Dunn
Formato:	article
Lenguaje:	EN ES
Publicado:	Universidad de San Buenaventura 2010
Materias:	Recognition memory maximum likelihood estimation signal detection theory mixture models high threshold models Psychology BF1-990
Acceso en línea:	https://doaj.org/article/5bdf50624d1849578f1df57083c75ade
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spelling	oai:doaj.org-article:5bdf50624d1849578f1df57083c75ade2021-11-25T02:23:59ZHow to fit models of recognition memory data using maximum likelihood.10.21500/20112084.8592011-20842011-7922https://doaj.org/article/5bdf50624d1849578f1df57083c75ade2010-06-01T00:00:00Zhttps://revistas.usb.edu.co/index.php/IJPR/article/view/859https://doaj.org/toc/2011-2084https://doaj.org/toc/2011-7922The aim of this paper is to provide an introductory tutorial to how to fit different models of recognition memory using maximum likelihood estimation. It is in four main parts. The first part describes how recognition memory data is collected and analysed. The second part introduces four current models that will be fitted to the data. The third part describes in detail how a model is fit using maximum likelihood estimation. The fourth part examines how the fit of a model can be evaluated and the appropriate statistical test applied.John C. DunnUniversidad de San BuenaventuraarticleRecognition memorymaximum likelihood estimationsignal detection theorymixture modelshigh threshold modelsPsychologyBF1-990ENESInternational Journal of Psychological Research, Vol 3, Iss 1 (2010)
institution	DOAJ
collection	DOAJ
language	EN ES
topic	Recognition memory maximum likelihood estimation signal detection theory mixture models high threshold models Psychology BF1-990
spellingShingle	Recognition memory maximum likelihood estimation signal detection theory mixture models high threshold models Psychology BF1-990 John C. Dunn How to fit models of recognition memory data using maximum likelihood.
description	The aim of this paper is to provide an introductory tutorial to how to fit different models of recognition memory using maximum likelihood estimation. It is in four main parts. The first part describes how recognition memory data is collected and analysed. The second part introduces four current models that will be fitted to the data. The third part describes in detail how a model is fit using maximum likelihood estimation. The fourth part examines how the fit of a model can be evaluated and the appropriate statistical test applied.
format	article
author	John C. Dunn
author_facet	John C. Dunn
author_sort	John C. Dunn
title	How to fit models of recognition memory data using maximum likelihood.
title_short	How to fit models of recognition memory data using maximum likelihood.
title_full	How to fit models of recognition memory data using maximum likelihood.
title_fullStr	How to fit models of recognition memory data using maximum likelihood.
title_full_unstemmed	How to fit models of recognition memory data using maximum likelihood.
title_sort	how to fit models of recognition memory data using maximum likelihood.
publisher	Universidad de San Buenaventura
publishDate	2010
url	https://doaj.org/article/5bdf50624d1849578f1df57083c75ade
work_keys_str_mv	AT johncdunn howtofitmodelsofrecognitionmemorydatausingmaximumlikelihood
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How to fit models of recognition memory data using maximum likelihood.

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