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Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates

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  • Gauss M. Cordeiro
  • Denise A. Botter
  • Lúcia P. Barroso
  • Silvia L. P. Ferrari

Abstract

We develop three corrected score tests for generalized linear models with dispersion covariates, thus generalizing the results of Cordeiro, Ferrari and Paula (1993) and Cribari‐Neto and Ferrari (1995). We present, in matrix notation, general formulae for the coefficients which define the corrected statistics. The formulae only require simple operations on matrices and can be used to obtain analytically closed‐form corrections for score test statistics in a variety of special generalized linear models with dispersion covariates. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. Two examples, namely, iid sampling without covariates on the mean or dispersion parameter oand one‐way classification models, are given. We also present some simulations where the three corrected tests perform better than the usual score test, the likelihood ratio test and its Bartlett corrected version. Finally, we present a numerical example for a data set discussed by Simonoff and Tsai (1994).

Suggested Citation

  • Gauss M. Cordeiro & Denise A. Botter & Lúcia P. Barroso & Silvia L. P. Ferrari, 2003. "Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(4), pages 391-409, November.
    • Handle: RePEc:bla:stanee:v:57:y:2003:i:4:p:391-409
    DOI: 10.1111/1467-9574.00237
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    Cited by:

    1. da Silva-Júnior, Antonio Hermes M. & da Silva, Damião Nóbrega & Ferrari, Silvia L. P., 2014. "mdscore: An R Package to Compute Improved Score Tests in Generalized Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(c02).
    2. Kakizawa, Yoshihide, 2012. "Improved chi-squared tests for a composite hypothesis," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 141-161.

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