Ascent EM for fast and global solutions to finite mixtures: An application to curve-clustering of online auctions

DOI10.1016/j.csda.2006.03.013zbMath1157.62437OpenAlexW2043326150MaRDI QIDQ1010411

Wolfgang Jank

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.03.013

zbMATH Keywords

clusteringaccelerationgenetic algorithmevolutionary computationglobal optimummixture modelfunctional dataMonte Carlo EMonline auctionebay

Mathematics Subject Classification ID

Related Items (2)

The REBMIX Algorithm for the Multivariate Finite Mixture Estimation ⋮ New global optimization algorithms for model-based clustering

Uses Software

• SemiPar
• Ox

Cites Work

• On the convergence properties of the EM algorithm
• The timing of bid placement and extent of multiple bidding: an empirical investigation using ebay online auctions
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Semiparametric Regression
• Unsupervised Curve Clustering using B‐Splines
• Clustering for Sparsely Sampled Functional Data
• An automated (Markov chain) Monte Carlo EM algorithm
• Ascent-Based Monte Carlo Expectation– Maximization
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