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Parameter estimation for generalized thurstone choice models

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  • Vojnovic, Milan
  • Yun, Seyoung

Abstract

We consider the maximum likelihood parameter estimation problem for a generalized Thurstone choice model, where choices are from comparison sets of two or more items. We provide tight characterizations of the mean square error, as well as necessary and sufficient conditions for correct classification when each item belongs to one of two classes. These results provide insights into how the estimation accuracy depends on the choice of a generalized Thurstone choice model and the structure of comparison sets. We find that for a priori unbiased structures of comparisons, e.g., when comparison sets are drawn independently and uniformly at random, the number of observations needed to achieve a prescribed estimation accuracy depends on the choice of a generalized Thurstone choice model. For a broad set of generalized Thurstone choice models, which includes all popular instances used in practice, the estimation error is shown to be largely insensitive to the cardinality of comparison sets. On the other hand, we found that there exist generalized Thurstone choice models for which the estimation error decreases much faster with the cardinality of comparison sets.

Suggested Citation

  • Vojnovic, Milan & Yun, Seyoung, 2016. "Parameter estimation for generalized thurstone choice models," LSE Research Online Documents on Economics 85703, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:85703
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    File URL: http://eprints.lse.ac.uk/85703/
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    1. -, 1978. "Procesamiento de los datos censales," Sede de la CEPAL en Santiago (Estudios e Investigaciones) 32269, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    2. Stern, Hal, 1992. "Are all linear paired comparison models empirically equivalent?," Mathematical Social Sciences, Elsevier, vol. 23(1), pages 103-117, February.
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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