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An approximate maximum likelihood procedure for parameter estimation in multivariate discrete data regression models

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  • Andrew Roddam

Abstract

This paper considers an alternative to iterative procedures used to calculate maximum likelihood estimates of regression coefficients in a general class of discrete data regression models. These models can include both marginal and conditional models and also local regression models. The classical estimation procedure is generally via a Fisher-scoring algorithm and can be computationally intensive for high-dimensional problems. The alternative method proposed here is non-iterative and is likely to be more efficient in high-dimensional problems. The method is demonstrated on two different classes of regression models.

Suggested Citation

  • Andrew Roddam, 2001. "An approximate maximum likelihood procedure for parameter estimation in multivariate discrete data regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(2), pages 273-279.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:273-279
    DOI: 10.1080/02664760020016163
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    References listed on IDEAS

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    1. Nanny Wermuth & D.R. Cox, 1998. "On the Application of Conditional Independence to Ordinal Data," International Statistical Review, International Statistical Institute, vol. 66(2), pages 181-199, August.
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