Convergence rate of MLE in generalized linear and nonlinear mixed-effects models: Theory and applications

By Lei Nie. 2007

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Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data (2014, background): “A more general asymptotic analysis is to study the convergence rates of both the mean function estimation and the individual curves prediction when the number of curves and/or the number of observations on each curve tend to infinity, as discussed in Nie (2007) for the maximum likelihood estimators ”
Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models (2016, methodology): “Condition C.12 can be verified for some usually used GLMMs and similar conditions have been discussed in Nie (2007) and Yu, Zhang, and Yau (2014).”
Maximum Likelihood Estimation for Stochastic Differential Equations with Random Effects (2013, methodology): “For theoretical properties of the MLE in the context of mixed eﬀects models, the main contribution to our knowledge is due to (Nie and Yang, 2005; Nie, 2006, 2007) and covers the asymptotic properties of the MLE for the population parameters under several asymptotic frameworks, depending on whether…”