Pseudo-score confidence intervals for parameters in discrete statistical models
We propose pseudo-score confidence intervals for parameters in models for discrete data. The confidence interval is obtained by inverting a test that uses a Pearson chi-squared statistic to compare fitted values for the working model with fitted values of the model when a parameter of interest takes various fixed values. For multinomial models, the pseudo-score method simplifies to the score method when the model is saturated and otherwise it is asymptotically equivalent to score and likelihood ratio test-based inferences. For cases in which ordinary score methods are impractical, such as when the likelihood function is not an explicit function of model parameters, the pseudo-score method is feasible. We illustrate the method for four such examples. Generalizations of the method are also presented for future research, including inference for complex sampling designs using a quasilikelihood Pearson statistic that compares fitted values for two models relative to the variance of the observations under the simpler model. Copyright 2010, Oxford University Press.
Year of publication: |
2010
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Authors: | Agresti, Alan ; Ryu, Euijung |
Published in: |
Biometrika. - Biometrika Trust, ISSN 0006-3444. - Vol. 97.2010, 1, p. 215-222
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Publisher: |
Biometrika Trust |
Saved in:
Online Resource
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