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

Andrew Roddam

Journal of Applied Statistics, 2001, vol. 28, issue 2, 273-279

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.

Date: 2001
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DOI: 10.1080/02664760020016163

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