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A modified candecomp algorithm for fitting the latent class model: Implementation and evaluation

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  • J. Douglas Carroll
  • Geert De Soete
  • Victor Kamensky

Abstract

In this paper an implementation is discussed of a modified CANDECOMP algorithm for fitting Lazarsfeld's latent class model. The CANDECOMP algorithm is modified such that the resulting parameter estimates are non‐negative and ‘best asymptotically normal’. In order to achieve this, the modified CANDECOMP algorithm minimizes a weighted least squares function instead of an unweighted least squares function as the traditional CANDECOMP algorithm does. To evaluate the new procedure, the modified CANDECOMP procedure with different weighting schemes is compared on five published data sets with the widely‐used iterative proportional fitting procedure for obtaining maximum likelihood estimates of the parameters in the latent class model. It is found that, with appropriate weights, the modified CANDECOMP algorithm yields solutions that are nearly identical with those obtained by means of the maximum likelihood procedure. While the modified CANDECOMP algorithm tends to be computationally more intensive than the maximum likelihood method, it is very flexible in that it easily allows one to try out different weighting schemes.

Suggested Citation

  • J. Douglas Carroll & Geert De Soete & Victor Kamensky, 1992. "A modified candecomp algorithm for fitting the latent class model: Implementation and evaluation," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 8(4), pages 303-309, December.
    • Handle: RePEc:wly:apsmda:v:8:y:1992:i:4:p:303-309
    DOI: 10.1002/asm.3150080405
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