4 citations · 10 across the 11 of their papers we have counts for
24 papers
Universal inference with composite likelihoods
Hien D Nguyen, Jessica Bagnall-Guerreiro, Andrew T Jones
Maximum composite likelihood estimation is a useful alternative to maximum likelihood estimation when data arise from data generating processes (DGPs) that do not admit tractable j…
Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models
Hien Duy Nguyen, TrungTin Nguyen, Faicel Chamroukhi +1
Mixture of experts (MoE) models are widely applied for conditional probability density estimation problems. We demonstrate the richness of the class of MoE models by proving densen…
Universal Inference with Composite Likelihoods
Hien Duy Nguyen
Wasserman et al. (2020, PNAS, vol. 117, pp. 16880-16890) constructed estimator agnostic and finite-sample valid confidence sets and hypothesis tests, using split-data likelihood ra…
A binary-response regression model based on support vector machines
Hien D Nguyen, Daniel V Fryer
The soft-margin support vector machine (SVM) is a ubiquitous tool for prediction of binary-response data. However, the SVM is characterized entirely via a numerical optimization pr…
Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
Faïcel Chamroukhi, Florian Lecocq, Hien D. Nguyen
Mixtures-of-Experts models and their maximum likelihood estimation (MLE) via the EM algorithm have been thoroughly studied in the statistics and machine learning literature. They a…
Approximate Bayesian computation via the energy statistic
Hien D. Nguyen, Julyan Arbel, Hongliang Lü +1
Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely u…