A random effects individual difference multidimensional scaling model

Recent algorithms for nonlinear mixed effects models can also be used, after some modification, to fit individual differences multidimensional scaling models in which the subject weights are random effects. The models we propose here have several advantages over models which do not use random effect...

Full description

Saved in:
Bibliographic Details
Published inComputational statistics & data analysis Vol. 32; no. 3; pp. 337 - 347
Main Author Clarkson, Douglas B
Format Journal Article Conference Proceeding
LanguageEnglish
Published Amsterdam Elsevier B.V 28.01.2000
Elsevier Science
Elsevier
SeriesComputational Statistics & Data Analysis
Subjects
Exact sciences and technology
Mathematics
Multivariate analysis
Probability and statistics
Sciences and techniques of general use
Statistics
Data analysis
Multidimensional analysis
Fitting
Mixed model
Random effect
Multivariate analysis
Non linear effect
Algorithm
Multidimensional scaling
Online AccessGet full text
ISSN0167-9473
1872-7352
DOI10.1016/S0167-9473(99)00087-0

Cover

More Information
Summary:Recent algorithms for nonlinear mixed effects models can also be used, after some modification, to fit individual differences multidimensional scaling models in which the subject weights are random effects. The models we propose here have several advantages over models which do not use random effects. For example, unlike traditional models, the number of parameters does not increase with the number of subjects, and, because the distribution of the subject weights is modeled, generalization of results to the sampled population of subjects is immediate. These models also have some disadvantages. Here, we discuss the proposed random effects model, give a computational algorithm for fitting the model, describe our experiences with this algorithm, and discuss potential generalizations to other multidimensional scaling models.
ISSN:0167-9473
1872-7352
DOI:10.1016/S0167-9473(99)00087-0