title: Parameter identification in multinomial processing tree models.
abstract: Multinomial processing tree models form a popular class of statistical models for categorical data that have applications in various areas of psychological research. As in all statistical models, establishing which parameters are identified is necessary for model inference and selection on the basis of the likelihood function, and for the interpretation of the results. The required calculations to establish global identification can become intractable in complex models. We show how to establish local identification in multinomial processing tree models, based on formal methods independently proposed by Catchpole and Morgan (1997) and by Bekker, Merckens, and Wansbeek (1994). This approach is illustrated with multinomial processing tree models for the source-monitoring paradigm in memory research.
authors.affiliation: Department of Psychological Methods, University of Amsterdam, Roetersstraat 15, Room A.514, 1018WB Amsterdam, The Netherlands. v.d.schmittmann@uva.nl
authors.forename: Verena D
authors.initials: VD
authors.lastname: Schmittmann
authors.forename: Conor V
authors.initials: CV
authors.lastname: Dolan
authors.forename: Maartje E J
authors.initials: ME
authors.lastname: Raijmakers
authors.forename: William H
authors.initials: WH
authors.lastname: Batchelder
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D006801:Humans; D016013:Likelihood Functions; D008568:Memory / Q000502:physiology; D015233:Models, Statistical; D013269:Stochastic Processes