Extreme deconvolution: inferring complete distribution functions from noisy, heterogeneous and incomplete observations

DOI10.1214/10-AOAS439zbMath1223.62029arXiv0905.2979OpenAlexW3100196865WikidataQ58196405 ScholiaQ58196405MaRDI QIDQ641112

David W. Hogg, Jo Bovy, Sam T. Roweis

Publication date: 21 October 2011

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0905.2979

zbMATH Keywords

noisedensity estimationmissing dataBayesian inferenceExpectation-Maximizationmultivariate estimation

Mathematics Subject Classification ID

Related Items (2)

Bayesian semiparametric multivariate density deconvolution via stochastic rotation of replicates ⋮ Bayesian Semiparametric Multivariate Density Deconvolution

Uses Software

• BayesDA

Cites Work

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• Density estimation with heteroscedastic error
• On the convergence properties of the EM algorithm
• Modeling by shortest data description
• Estimating the dimension of a model
• Fourier methods for estimating mixing densities and distributions
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Deconvolving kernel density estimators
• The segmental K-means algorithm for estimating parameters of hidden Markov models
• Density Estimation in the Presence of Heteroscedastic Measurement Error
• Likelihood analysis for probit regression with measurement errors
• Likelihood analysis for errors-in-variables regression with replicate measurements
• Measurement Error in Nonlinear Models
• An Information Measure for Classification
• Split and merge EM algorithm for improving Gaussian mixture density estimates

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