Non-asymptotic bounds for quasi-MLE, misspecified models, and dependence under group sequential sampling

Julian Aronowitz; Jay Bartroff

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arXiv · 2606.07499

Non-asymptotic bounds for quasi-MLE, misspecified models, and dependence under group sequential sampling

Julian Aronowitz·Jay Bartroff

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Abstract

We derive asymptotic multivariate normal limits and explicit non-asymptotic normal approximation bounds for group sequential quasi-maximum likelihood estimators under possible model misspecification and within-group dependence. The bounds, obtained using Stein's method, have known constants and apply to a class of dependent-data estimating problems in which the likelihood used for estimation may differ from the true data-generating mechanism. We compute the limiting covariance structure and finite-sample bound explicitly for a Poisson generalized linear mixed model with random group effects and illustrate the results using data from an epilepsy clinical trial.

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Julian Aronowitz, Jay Bartroff. 2026-06-05. Non-asymptotic bounds for quasi-MLE, misspecified models, and dependence under group sequential sampling. https://arxiv.org/abs/2606.07499

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