5 papers
Stabilised weighted data subsampling for accelerated Bayesian inference in models with recursive likelihoods
Matias Quiroz, Aishwarya Bhaskaran, Zixuan Wang +1
Bayesian inference for models with recursively defined likelihoods is computationally demanding, limiting scalability to large datasets. We propose a stabilised weighted subsamplin…
A maximum penalised likelihood approach for semiparametric accelerated failure time models with time-varying covariates and partly interval censoring
Aishwarya Bhaskaran, Ding Ma, Benoit Liquet +4
Accelerated failure time (AFT) models are frequently used to model survival data, providing a direct quantification of the relationship between event times and covariates. These mo…
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…
Conditionally structured variational Gaussian approximation with importance weights
Linda S. L. Tan, Aishwarya Bhaskaran, David J. Nott
We develop flexible methods of deriving variational inference for models with complex latent variable structure. By splitting the variables in these models into "global" parameters…