Model‐based clustering of regression time series data via APECM—an AECM algorithm sung to an even faster beat

DOI10.1002/sam.10143OpenAlexW2118150576WikidataQ57709244 ScholiaQ57709244MaRDI QIDQ4969810

Wei-Chen Chen, Ranjan Maitra

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.10143

zbMATH Keywords

EM algorithmmodel-based clusteringautoregressive modelfinite mixture modelmutual fundsAPECM

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (4)

Finite mixture modeling of Gaussian regression time series with application to dendrochronology ⋮ A double clustering algorithm for financial time series based on extreme events ⋮ Semi-supervised model-based clustering with positive and negative constraints ⋮ Efficient estimation in model‐based clustering of Gaussian regression time series

Uses Software

• S-PLUS
• MEMSS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Finite mixture models and model-based clustering
• Statistical theory in clustering
• Estimating the dimension of a model
• Approximate single linkage cluster analysis of large data sets in high-dimensional spaces
• A mixture likelihood approach for generalized linear models
• Maximum likelihood estimation via the ECM algorithm: A general framework
• The EM Algorithm and Extensions, 2E
• Finding Groups in Data
• Model-Based Gaussian and Non-Gaussian Clustering
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Mixed-Effects Models in S and S-PLUS
• Generalized Least Squares
• On a Mixture Autoregressive Model
• A Family of Variable-Metric Methods Derived by Variational Means
• A new approach to variable metric algorithms
• The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
• Conditioning of Quasi-Newton Methods for Function Minimization
• A Simplex Method for Function Minimization
• A new look at the statistical model identification

This page was built for publication: Model‐based clustering of regression time series data via APECM—an AECM algorithm sung to an even faster beat