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02/26/2023
Semi-supervised Gaussian mixture modelling with a missing-data mechanism in R
Semi-supervised learning is being extensively applied to estimate classi...
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10/25/2022
Some Simulation and Empirical Results for Semi-Supervised Learning of the Bayes Rule of Allocation
There has been increasing attention to semi-supervised learning (SSL) ap...
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02/04/2022
Functional Mixtures-of-Experts
We consider the statistical analysis of heterogeneous data for clusterin...
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04/08/2021
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review
There has been increasing attention to semi-supervised learning (SSL) ap...
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04/07/2021
Data-fusion using factor analysis and low-rank matrix completion
Data-fusion involves the integration of multiple related datasets. The s...
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04/07/2021
Harmless label noise and informative soft-labels in supervised classification
Manual labelling of training examples is common practice in supervised l...
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09/22/2020
An l_1-oracle inequality for the Lasso in mixture-of-experts regression models
Mixture-of-experts (MoE) models are a popular framework for modeling het...
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08/22/2020
Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces
The class of location-scale finite mixtures is of enduring interest both...
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05/14/2020
On mean and/or variance mixtures of normal distributions
Parametric distributions are an important part of statistics. There is n...
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05/14/2020
Multi-Node EM Algorithm for Finite Mixture Models
Finite mixture models are powerful tools for modelling and analyzing het...
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04/13/2020
Estimation of Classification Rules from Partially Classified Data
We consider the situation where the observed sample contains some observ...
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10/21/2019
An Apparent Paradox: A Classifier Trained from a Partially Classified Sample May Have Smaller Expected Error Rate Than That If the Sample Were Completely Classified
There has been increasing interest in using semi-supervised learning to ...
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04/26/2019
Comment on "Hidden truncation hyperbolic distributions, finite mixtures thereof and their application for clustering" Murray, Browne, and
We comment on the paper of Murray, Browne, and McNicholas (2017), who pr...
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04/05/2019
On missing label patterns in semi-supervised learning
We investigate model based classification with partially labelled traini...
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03/29/2019
Statistical matching of non-Gaussian data
The statistical matching problem is a data integration problem with stru...
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03/01/2019
Approximation by finite mixtures of continuous density functions that vanish at infinity
Given sufficiently many components, it is often cited that finite mixtur...
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10/11/2018
On formulations of skew factor models: skew errors versus skew factors
In the past few years, there have been a number of proposals for general...
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05/11/2018
False Discovery Rate Control Under Reduced Precision Computation
The mitigation of false positives is an important issue when conducting ...
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04/23/2018
Positive data kernel density estimation via the logKDE package for R
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric ...
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04/23/2018
Log-transformed kernel density estimation for positive data
Kernel density estimators (KDEs) are ubiquitous tools for nonpara- metri...
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04/23/2018
Randomized Mixture Models for Probability Density Approximation and Estimation
Randomized neural networks (NNs) are an interesting alternative to conve...
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02/07/2018
Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions
Mixtures of factor analyzers (MFA) provide a powerful tool for modelling...
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11/18/2017
Deep Gaussian Mixture Models
Deep learning is a hierarchical inference method formed by subsequent mu...
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05/12/2017
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach
Support vector machines (SVMs) are an important tool in modern data anal...
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02/11/2016
A Universal Approximation Theorem for Mixture of Experts Models
The mixture of experts (MoE) model is a popular neural network architect...
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11/11/2014
Supervised Classification of Flow Cytometric Samples via the Joint Clustering and Matching (JCM) Procedure
We consider the use of the Joint Clustering and Matching (JCM) procedure...
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05/31/2013
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
In systems biomedicine, an experimenter encounters different potential s...
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11/02/2010
