Identifying intrinsic variability in multivariate systems through linearized inverse methods

DOI10.1080/17415971003624330zbMath1187.62205OpenAlexW2146242355MaRDI QIDQ3565080

Yannick Lefebvre, Agnès Grimaud, Étienne De Rocquigny, Gilles Celeux

Publication date: 27 May 2010

Published in: Inverse Problems in Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/17415971003624330

zbMATH Keywords

linear approximationidentifiabilityuncertainty modelingintrinsic variabilityEM and ECME algorithms

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (1)

Nonlinear methods for inverse statistical problems

Cites Work

• Maximum likelihood estimation via the ECM algorithm: A general framework
• Inverse probabilistic modelling of the sources of uncertainty: a non-parametric simulated-likelihood method with application to an industrial turbine vibration assessment
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence

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