Open AccessA marginal maximum likelihood method for the vector threshold model to analyze dichotomous choice data
Author(s) -
Hojo Hiroshi
Publication year - 2003
Publication title -
japanese psychological research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.392
H-Index - 30
eISSN - 1468-5884
pISSN - 0021-5368
DOI - 10.1111/1468-5884.t01-1-00044
Subject(s) - threshold model , expectation–maximization algorithm , mathematics , maximum likelihood , marginal likelihood , maximization , statistics , variable (mathematics) , econometrics , computer science , mathematical optimization , mathematical analysis
At least two types of models, the vector model and the unfolding model can be used for the analysis of dichotomous choice data taken from, for example, the pick any/ n method. The previous vector threshold models have a difficulty with estimation of the nuisance parameters such as the individual vectors and thresholds. This paper proposes a new probabilistic vector threshold model, where, unlike the former vector models, the angle that defines an individual vector is a random variable, and where the marginal maximum likelihood estimation method using the expectation‐maximization algorithm is adopted to avoid incidental parameters. The paper also attempts to discuss which of the two models is more appropriate to account for dichotomous choice data. Two sets of dichotomous choice data are analyzed by the model.