71 citations · 110 across the 48 of their papers we have counts for
13 papers · 1 filter
Chunked-and-Averaged Estimators for Vector Parameters
Hien D. Nguyen, Geoffrey J. McLachlan
A divide-and-conquer method for parameter estimation is the chunked-and-averaged (CA) estimator. CA estimators have been studied for univariate parameters under independent and ide…
An Introduction to MM Algorithms for Machine Learning and Statistical
Hien D. Nguyen
MM (majorization--minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This article introduce…
On approximations via convolution-defined mixture models
Hien D. Nguyen, Geoffrey J. McLachlan
An often-cited fact regarding mixing or mixture distributions is that their density functions are able to approximate the density function of any unknown distribution to arbitrary…
Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling
Hien D. Nguyen, Jeremy F. P. Ullmann, Geoffrey J. McLachlan +5
Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescen…
Faster Functional Clustering via Gaussian Mixture Models
Hien D Nguyen, Geoffrey J McLachlan, Jeremy F P Ullmann +1
Functional data analysis (FDA) is an important modern paradigm for handling infinite-dimensional data. An important task in FDA is model-based clustering, which organizes functiona…
Progress on a Conjecture Regarding the Triangular Distribution
Hien D Nguyen, Geoffrey J McLachlan
Triangular distributions are a well-known class of distributions that are often used as an elementary example of a probability model. Maximum likelihood estimation of the mode para…
A globally convergent algorithm for lasso-penalized mixture of linear regression models
Luke R. Lloyd-Jones, Hien D. Nguyen, Geoffrey J. McLachlan
Variable selection is an old and pervasive problem in regression analysis. One solution is to impose a lasso penalty to shrink parameter estimates toward zero and perform continuou…
A Block Minorization--Maximization Algorithm for Heteroscedastic Regression
Hien D. Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan
The computation of the maximum likelihood (ML) estimator for heteroscedastic regression models is considered. The traditional Newton algorithms for the problem require matrix multi…
Linear Mixed Models with Marginally Symmetric Nonparametric Random Effects
Hien D. Nguyen, Geoffrey J. McLachlan
Linear mixed models (LMMs) are used as an important tool in the data analysis of repeated measures and longitudinal studies. The most common form of LMMs utilize a normal distribut…
Maximum Likelihood Estimation of Triangular and Polygonal Distributions
Hien D Nguyen, Geoffrey J McLachlan
Triangular distributions are a well-known class of distributions that are often used as elementary example of a probability model. In the past, enumeration and order statistic-base…
A Universal Approximation Theorem for Mixture of Experts Models
Hien D Nguyen, Luke R Lloyd-Jones, Geoffrey J McLachlan
The mixture of experts (MoE) model is a popular neural network architecture for nonlinear regression and classification. The class of MoE mean functions is known to be uniformly co…
Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data
Hien D Nguyen, Geoffrey J McLachlan, Pierre Orban +2
Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires the ev…
Spatial Clustering of Time-Series via Mixture of Autoregressions Models and Markov Random Fields
Hien D Nguyen, Geoffrey J McLachlan, Jeremy F P Ullmann +1
Time-series data arise in many medical and biological imaging scenarios. In such images, a time-series is obtained at each of a large number of spatially-dependent data units. It i…