2014/09/01 by Abel Rodriguez, David B. Dunson
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Cluster analysis #Dirichlet distribution #Dirichlet process #Functional data analysis #Generalization #Partition (number theory) #Statistical Methods and Bayesian Inference #stat.AP
paper · pdf · doi:10.1214/14-aoas751
published as Annals of Applied Statistics 2014, Vol. 8, No. 3, 1416-1442 · Published in at http://dx.doi.org/10.1214/14-AOAS751 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2014/09/01 · arxiv created 2014/11/20 · arxiv updated 2014/11/21 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We discuss functional clustering procedures for nested designs, where multiple curves are collected for each subject in the study. We start by considering the application of standard functional clustering tools to this problem, which leads to groupings based on the average profile for each subject. After discussing some of the shortcomings of this approach, we present a mixture model based on a generalization of the nested Dirichlet process that clusters subjects based on the distribution of their curves. By using mixtures of generalized Dirichlet processes, the model induces a much more flexible prior on the partition structure than other popular model-based clustering methods, allowing for different rates of introduction of new clusters as the number of observations increases. The methods are illustrated using hormone profiles from multiple menstrual cycles collected for women in the Early Pregnancy Study.