Literature DB >> 16709283

Selecting among three-mode principal component models of different types and complexities: a numerical convex hull based method.

Eva Ceulemans1, Henk A L Kiers.   

Abstract

Several three-mode principal component models can be considered for the modelling of three-way, three-mode data, including the Candecomp/Parafac, Tucker3, Tucker2, and Tucker1 models. The following question then may be raised: given a specific data set, which of these models should be selected, and at what complexity (i.e. with how many components)? We address this question by proposing a numerical model selection heuristic based on a convex hull. Simulation results show that this heuristic performs almost perfectly, except for Tucker3 data arrays with at least one small mode and a relatively large amount of error.

Mesh:

Year:  2006        PMID: 16709283     DOI: 10.1348/000711005X64817

Source DB:  PubMed          Journal:  Br J Math Stat Psychol        ISSN: 0007-1102            Impact factor:   3.380


  18 in total

1.  Modeling differences in the dimensionality of multiblock data by means of clusterwise simultaneous component analysis.

Authors:  Kim De Roover; Eva Ceulemans; Marieke E Timmerman; John B Nezlek; Patrick Onghena
Journal:  Psychometrika       Date:  2013-01-25       Impact factor: 2.500

2.  Three-mode factor analysis by means of Candecomp/Parafac.

Authors:  Alwin Stegeman; Tam T T Lam
Journal:  Psychometrika       Date:  2013-11-23       Impact factor: 2.500

3.  Investigating the interplay between parenting dimensions and styles, and the association with adolescent outcomes.

Authors:  Filip Calders; Patricia Bijttebier; Guy Bosmans; Eva Ceulemans; Hilde Colpin; Luc Goossens; Wim Van Den Noortgate; Karine Verschueren; Karla Van Leeuwen
Journal:  Eur Child Adolesc Psychiatry       Date:  2019-05-30       Impact factor: 4.785

4.  Simultaneous Component Analysis by Means of Tucker3.

Authors:  Alwin Stegeman
Journal:  Psychometrika       Date:  2017-04-06       Impact factor: 2.500

5.  Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View.

Authors:  Edoardo Saccenti; Marieke E Timmerman
Journal:  Psychometrika       Date:  2016-10-13       Impact factor: 2.500

6.  Potential mobility assessment of metals in salt marsh sediments from San Antonio Bay.

Authors:  Carmen H Marinho; Erica Giarratano; Claudia E Domini; Mariano Garrido; Mónica N Gil
Journal:  Environ Monit Assess       Date:  2019-11-06       Impact factor: 2.513

7.  A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models.

Authors:  Ginette Lafit; Kristof Meers; Eva Ceulemans
Journal:  Psychometrika       Date:  2021-11-01       Impact factor: 2.500

8.  How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa.

Authors:  Leonie V D E Vogelsmeier; Jeroen K Vermunt; Kim De Roover
Journal:  Behav Res Methods       Date:  2022-09-01

9.  DeCon: a tool to detect emotional concordance in multivariate time series data of emotional responding.

Authors:  Kirsten Bulteel; Eva Ceulemans; Renee J Thompson; Christian E Waugh; Ian H Gotlib; Francis Tuerlinckx; Peter Kuppens
Journal:  Biol Psychol       Date:  2013-11-09       Impact factor: 3.251

10.  Detecting which variables alter component interpretation across multiple groups: A resampling-based method.

Authors:  Sopiko Gvaladze; Kim De Roover; Francis Tuerlinckx; Eva Ceulemans
Journal:  Behav Res Methods       Date:  2020-02
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.