3 papers
stat.ME2026
Stabilised weighted data subsampling for accelerated inference in models with recursive likelihoods
Matias Quiroz, Aishwarya Bhaskaran, Zixuan Wang +1
Inference for models with recursively defined likelihoods is computationally demanding, limiting scalability to large datasets. We propose a stabilised weighted subsampling methodo…
stat.ME2024
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…
stat.CO2019
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…