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Strategies for Non-Parametric Smoothing of the Location Model in Mixed-Variable Discriminant Analysis

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Listed:
  • Nor Mahat
  • W.J. Krzanowski
  • A. Hernandez

Abstract

The non-parametric smoothing of the location model proposed by Asparoukhov and Krzanowski (2000) for allocating objects with mixtures of variables into two groups is studied. The strategy for selecting the smoothing parameter through the maximisation of the pseudo-likelihood function is reviewed. Problems with previous methods are highlighted, and two alternative strategies are proposed. Some investigations into other possible smoothing procedures for estimating cell probabilities are discussed. A leave-one-out method is proposed for constructing the allocation rule and evaluating its performance by estimating the true error rate. Results of a numerical study on simulated data highlight the feasibility of the proposed allocation rule as well as its advantages over previous methods, and an example using real data is presented.

Suggested Citation

  • Nor Mahat & W.J. Krzanowski & A. Hernandez, 2009. "Strategies for Non-Parametric Smoothing of the Location Model in Mixed-Variable Discriminant Analysis," Modern Applied Science, Canadian Center of Science and Education, vol. 3(1), pages 151-151, January.
    • Handle: RePEc:ibn:masjnl:v:3:y:2009:i:1:p:151
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    References listed on IDEAS

    as
    1. W. Krzanowski, 1993. "The location model for mixtures of categorical and continuous variables," Journal of Classification, Springer;The Classification Society, vol. 10(1), pages 25-49, January.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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