The gradient test and its finite sample size properties in a conditional maximum likelihood and psychometric modeling context

DOI10.1080/03610918.2019.1710193zbMath1489.62366OpenAlexW2999268345WikidataQ126378928 ScholiaQ126378928MaRDI QIDQ5866149

Andreas Kurz, Clemens Draxler, Artur J. Lemonte

Publication date: 13 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1710193

zbMATH Keywords

conditional maximum likelihoodRasch modelpsychometricsgradient test

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Parametric hypothesis testing (62F03) Point estimation (62F10) Applications of statistics to psychology (62P15)

Related Items (1)

Power analysis for the Wald, LR, score, and gradient tests in a marginal maximum likelihood framework: applications in IRT

Uses Software

• R

Cites Work

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• The local power of the gradient test
• Sample size determination for Rasch model tests
• Sample size determination within the scope of conditional maximum likelihood estimation with special focus on testing the Rasch model
• On the existence and uniqueness of maximum-likelihood estimates in the Rasch model
• On the consistency of conditional maximum likelihood estimators
• The Lagrangian Multiplier Test
• The local power of the efficient scores test statistic
• The likelihood ratio criterion for a composite hypothesis under a local alternative
• The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
• Consistent Estimates Based on Partially Consistent Observations
• Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large

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