Parametric models

  • R. Harald Baayen
Part of the Text, Speech and Language Technology book series (TLTB, volume 18)


This chapter introduces three families of LNRE models: Carroll's (1967) log-normal model, Sichel's (1975) generalized inverse Gauss-Poisson model, and Orlov and Chitashvili's (1983a,b) extended Zipf's law. By enriching the non- parametric expressions of the preceding chapter with parametric assumptions about the shape of the structural distribution, we can avoid the technical problems associated with the non-parametric methods. Section 3.2 describes the three LNRE models. Sections 3.3 and 3.6 discuss techniques for evaluating the goodness of fit of the LNRE models and for testing whether word frequency distributions are different by means of statistics such as V(N) and V(1, N).


Mean Square Error Spectrum Element Vocabulary Size Lognormal Model British National Corpus 
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  1. 3.
    See Luke (1962) for details on integrate of Bessel functions.Google Scholar
  2. 5.
    For the generalized inverse Gauss-Poisson model, expressions and algorithms for parameter estimation based on the log-likelihood function are available (see Stein, Zucchini, and Juritz, 1987, Heller, 1997, and Burrell and Fenton, 1993).Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • R. Harald Baayen
    • 1
  1. 1.University of NijmegenThe Netherlands

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