Model-based classification via mixtures of multivariate t-distributions. - Jeffrey L. Andrews; Paul D. McNicholas; Sanjeena Subedi (Comput. Stat. Data Anal., 2011)

Extending mixtures of multivariate t-factor analyzers. - Jeffrey L. Andrews; Paul D. McNicholas (Stat. Comput., 2011)

Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions - The tEIGEN family. - Jeffrey L. Andrews; Paul D. McNicholas (Stat. Comput., 2012)

Using evolutionary algorithms for model-based clustering. - Jeffrey L. Andrews; Paul D. McNicholas (Pattern Recognit. Lett., 2013)

Variable Selection for Clustering and Classification. - Jeffrey L. Andrews; Paul D. McNicholas (J. Classif., 2014)

Addressing overfitting and underfitting in Gaussian model-based clustering. - Jeffrey L. Andrews (Comput. Stat. Data Anal., 2018)

Longitudinal linear combination test for gene set analysis. - Elham Khodayari Moez; Morteza Hajihosseini; Jeffrey L. Andrews; Irina Dinu (BMC Bioinform., 2019)

On Assessments of Agreement Between Fuzzy Partitions. - Jeffrey L. Andrews; Ryan P. Browne; Chelsey D. Hvingelby (J. Classif., 2022)

An artificial bee colony algorithm for mixture model-based clustering. - Anthony E. Culos; Jeffrey L. Andrews; Hamid Afshari (Commun. Stat. Simul. Comput., 2022)

Random Models for Fuzzy Clustering Similarity Measures. - Ryan DeWolfe; Jeffrey L. Andrews (CoRR, 2023)

Assessing the variability of posterior probabilities in Gaussian model-based clustering. - Yuchi Zhang; Ryan P. Browne; Jeffrey L. Andrews (Commun. Stat. Simul. Comput., 2023)

Random models for adjusting fuzzy rand index extensions. - Ryan DeWolfe; Jeffrey L. Andrews (Adv. Data Anal. Classif., 2025)