Ascent EM for fast and global solutions to finite mixtures: An application to curve-clustering of online auctions
From MaRDI portal
Publication:1010411
DOI10.1016/j.csda.2006.03.013zbMath1157.62437OpenAlexW2043326150MaRDI QIDQ1010411
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
clusteringaccelerationgenetic algorithmevolutionary computationglobal optimummixture modelfunctional dataMonte Carlo EMonline auctionebay
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (2)
The REBMIX Algorithm for the Multivariate Finite Mixture Estimation ⋮ New global optimization algorithms for model-based clustering
Uses Software
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
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Ascent EM for fast and global solutions to finite mixtures: An application to curve-clustering of online auctions
