136 citations · 255 across the 15 of their papers we have counts for
6 papers · 1 filter
Second Term Improvement to Generalised Linear Mixed Model Asymptotics
Luca Maestrini, Aishwarya Bhaskaran, Matt P. Wand
A recent article on generalised linear mixed model asymptotics, Jiang et al. (2022), derived the rates of convergence for the asymptotic variances of maximum likelihood estimators.…
Dispersion Parameter Extension of Precise Generalized Linear Mixed Model Asymptotics
Aishwarya Bhaskaran, Matt P. Wand
We extend a recently established asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The new results show that the maximum likelih…
Solutions to Multilevel Sparse Matrix Problems
Tui H. Nolan, Matt P. Wand
We define and solve classes of sparse matrix problems that arise in multilevel modeling and data analysis. The classes are indexed by the number of nested units, with two-level pro…
Asymptotic normality and valid inference for Gaussian variational approximation
Peter Hall, Tung Pham, M. P. Wand +1
We derive the precise asymptotic distributional behavior of Gaussian variational approximate estimators of the parameters in a single-predictor Poisson mixed model. These results a…
Asymptotics and optimal bandwidth selection for highest density region estimation
R. J. Samworth, M. P. Wand
We study kernel estimation of highest-density regions (HDR). Our main contributions are two-fold. First, we derive a uniform-in-bandwidth asymptotic approximation to a risk that is…
General Design Bayesian Generalized Linear Mixed Models
Y. Zhao, J. Staudenmayer, B. A. Coull +1
Linear mixed models are able to handle an extraordinary range of complications in regression-type analyses. Their most common use is to account for within-subject correlation in lo…