Ryan P. Browne

According to our database, Ryan P. Browne authored at least 34 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions.

Ryan P. Browne, Luca Bagnato, Antonio Punzo

Adv. Data Anal. Classif., September, 2024

Model-Based Clustering with Nested Gaussian Clusters.

Jason Hou-Liu, Ryan P. Browne

J. Classif., March, 2024

Flexible mixture regression with the generalized hyperbolic distribution.

Nam-Hwui Kim, Ryan P. Browne

Adv. Data Anal. Classif., March, 2024

2023

Generalized linear models for massive data via doubly-sketching.

Jason Hou-Liu, Ryan P. Browne

Stat. Comput., October, 2023

A dual subspace parsimonious mixture of matrix normal distributions.

Alex Sharp, Glen Chalatov, Ryan P. Browne

Adv. Data Anal. Classif., September, 2023

Assessing the variability of posterior probabilities in Gaussian model-based clustering.

Yuchi Zhang, Ryan P. Browne, Jeffrey L. Andrews

Commun. Stat. Simul. Comput., May, 2023

Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions.

Utkarsh J. Dang, Michael P. B. Gallaugher, Ryan P. Browne, Paul D. McNicholas

J. Classif., April, 2023

2022

A joint latent factor analyzer and functional subspace model for clustering multivariate functional data.

Alex Sharp, Ryan P. Browne

Stat. Comput., 2022

Chimeral Clustering.

Jason Hou-Liu, Ryan P. Browne

J. Classif., 2022

On Assessments of Agreement Between Fuzzy Partitions.

Jeffrey L. Andrews, Ryan P. Browne, Chelsey D. Hvingelby

J. Classif., 2022

Factor and hybrid components for model-based clustering.

Jason Hou-Liu, Ryan P. Browne

Adv. Data Anal. Classif., 2022

2021

Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package.

Cristina Tortora, Ryan P. Browne, Aisha Elsherbiny, Brian C. Franczak, Paul D. McNicholas

J. Stat. Softw., 2021

In the pursuit of sparseness: A new rank-preserving penalty for a finite mixture of factor analyzers.

Nam-Hwui Kim, Ryan P. Browne

Comput. Stat. Data Anal., 2021

Functional data clustering by projection into latent generalized hyperbolic subspaces.

Alex Sharp, Ryan P. Browne

Adv. Data Anal. Classif., 2021

One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes.

Ilia Sucholutsky, Nam-Hwui Kim, Ryan P. Browne, Matthias Schonlau

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Mixtures of Hidden Truncation Hyperbolic Factor Analyzers.

Paula M. Murray, Ryan P. Browne, Paul D. McNicholas

J. Classif., 2020

2019

Note of Clarification on 'Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering', by Murray, Browne, and McNicholas, J. Multivariate Anal. 161 (2017) 141-156.

Paula M. Murray, Ryan P. Browne, Paul D. McNicholas

J. Multivar. Anal., 2019

Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions.

Katherine Morris, Antonio Punzo, Paul D. McNicholas, Ryan P. Browne

Comput. Stat. Data Anal., 2019

A Mixture of Coalesced Generalized Hyperbolic Distributions.

Cristina Tortora, Brian C. Franczak, Ryan P. Browne, Paul D. McNicholas

J. Classif., 2019

Subspace clustering for the finite mixture of generalized hyperbolic distributions.

Nam-Hwui Kim, Ryan P. Browne

Adv. Data Anal. Classif., 2019

2017

Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering.

Paula M. Murray, Ryan P. Browne, Paul D. McNicholas

J. Multivar. Anal., 2017

Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models.

Utkarsh J. Dang, Antonio Punzo, Paul D. McNicholas, Salvatore Ingrassia, Ryan P. Browne

J. Classif., 2017

2016

A mixture of generalized hyperbolic factor analyzers.

Cristina Tortora, Paul D. McNicholas, Ryan P. Browne

Adv. Data Anal. Classif., 2016

2015

Corrigendum to "Unsupervised learning via mixtures of skewed distributions with hypercube contours" [Pattern Recognition Letters. 58(1), 69-76].

Brian C. Franczak, Cristina Tortora, Ryan P. Browne, Paul D. McNicholas

Pattern Recognit. Lett., 2015

Unsupervised learning via mixtures of skewed distributions with hypercube contours.

Brian C. Franczak, Cristina Tortora, Ryan P. Browne, Paul D. McNicholas

Pattern Recognit. Lett., 2015

Model based clustering of high-dimensional binary data.

Yang Tang, Ryan P. Browne, Paul D. McNicholas

Comput. Stat. Data Anal., 2015

2014

Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models.

Ryan P. Browne, Paul D. McNicholas

Stat. Comput., 2014

Mixtures of Shifted AsymmetricLaplace Distributions.

Brian C. Franczak, Ryan P. Browne, Paul D. McNicholas

IEEE Trans. Pattern Anal. Mach. Intell., 2014

Mixtures of skew-t factor analyzers.

Paula M. Murray, Ryan P. Browne, Paul D. McNicholas

Comput. Stat. Data Anal., 2014

Estimating common principal components in high dimensions.

Ryan P. Browne, Paul D. McNicholas

Adv. Data Anal. Classif., 2014

2013

Discussion of 'Model-based clustering and classification with non-normal mixture distributions' by Lee and McLachlan.

Paul D. McNicholas, Ryan P. Browne, Paula M. Murray

Stat. Methods Appl., 2013

2012

Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions.

Ryan P. Browne, Paul D. McNicholas, Matthew D. Sparling

IEEE Trans. Pattern Anal. Mach. Intell., 2012

2010

Leveraged Gauge R&R Studies.

Ryan P. Browne, R. Jock MacKay, Stefan H. Steiner

Technometrics, 2010

2009

Two-Stage Leveraged Measurement System Assessment.

Ryan P. Browne, R. Jock MacKay, Stefan H. Steiner

Technometrics, 2009