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An Evaluation of Two Algorithms for Hierarchical Classes Analysis

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  • Iwin Leenen
  • Iven Van Mechelen

Abstract

In this procedure, a least-squares loss function in terms of discrepancies between D and M is minimized. The present paper describes the original hierarchical classes algorithm proposed by De Boeck and Rosenberg (1988), which is based on an alternating greedy heuristic, and proposes a new algorithm, based on an alternating branch-and-bound procedure. An extensive simulation study is reported in which both algorithms are evaluated and compared according to goodness-of-fit to the data and goodness-of-recovery of the underlying true structure. Furthermore, three heuristics for selecting models of different ranks for a given D are presented and compared. The simulation results show that the new algorithm yields models with slightly higher goodness-of-fit and goodness-of-recovery values. Copyright Springer-Verlag New York Inc. 2001

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  • Iwin Leenen & Iven Van Mechelen, 2001. "An Evaluation of Two Algorithms for Hierarchical Classes Analysis," Journal of Classification, Springer;The Classification Society, vol. 18(1), pages 57-80, January.
    • Handle: RePEc:spr:jclass:v:18:y:2001:i:1:p:57-80
    DOI: 10.1007/s00357-001-0005-2
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    Citations

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    Cited by:

    1. Eva Vande Gaer & Eva Ceulemans & Iven Mechelen & Peter Kuppens, 2012. "The CLASSI-N Method for the Study of Sequential Processes," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 85-105, January.
    2. Tom Wilderjans & Eva Ceulemans & Iven Mechelen, 2008. "The CHIC Model: A Global Model for Coupled Binary Data," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 729-751, December.
    3. Alfonso Gutierrez-Lopez & Carlos Chávez & Carlos Díaz-Delgado, 2022. "Autocorrelation Ratio as a Measure of Inertia for the Classification of Extreme Events," Mathematics, MDPI, vol. 10(12), pages 1-15, June.
    4. Tom Wilderjans & E. Ceulemans & I. Mechelen, 2012. "The SIMCLAS Model: Simultaneous Analysis of Coupled Binary Data Matrices with Noise Heterogeneity Between and Within Data Blocks," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 724-740, October.
    5. Iven Mechelen & Luigi Lombardi & Eva Ceulemans, 2007. "Hierarchical Classes Modeling of Rating Data," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 475-488, December.
    6. Eva Ceulemans & Iven Mechelen & Iwin Leenen, 2007. "The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 377-391, September.
    7. Iwin Leenen & Iven Mechelen & Andrew Gelman & Stijn Knop, 2008. "Bayesian Hierarchical Classes Analysis," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 39-64, March.

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