A Review of Score-Test-Based Inference for Categorical Data

Alan Agresti (), Sabrina Giordano and Anna Gottard
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Alan Agresti: University of Florida
Sabrina Giordano: University of Calabria
Anna Gottard: University of Florence

Journal of Quantitative Economics, 2022, vol. 20, issue 1, No 3, 48 pages

Abstract: Abstract One of C. R. Rao’s many important contributions to statistical science was his introduction of the score test, based on the derivative of the log-likelihood function at the null hypothesis value of the parameter of interest. This article reviews methods for constructing score tests and score-test-based confidence intervals for analyzing parameters that arise in analyzing categorical data. A considerable literature indicates that score tests and their inversion for constructing confidence intervals perform well in a variety of settings and sometimes much better than Wald-test and likelihood-ratio test-based methods. We also discuss extensions of score-based inference and potential future research on generalizations for longitudinal data, complex sampling, and high-dimensional data.

Keywords: Confidence intervals; Generalized linear models; Likelihood-ratio tests; Pearson chi-squared; C.R. Rao; Wald inference (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40953-022-00309-8

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