Regularized error-in-variable estimation for big data modeling and process analytics

Uwe Kruger, Xun Wang, Mark J. Embrechts, Ali Almansoori, Juergen Hahn

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This article addresses estimating the uncertainty in operational data by introducing a regularized modeling technique. Existing work (i) requires knowing the true dimension of the operational data, (ii) relies on a maximum likelihood estimation that is compromised by a stringent restriction for this true dimension and (iii) is computationally expensive. In contrast, the presented regularized error-in-variable technique (i) allows determining the true data dimension through hypothesis testing, (ii) is not limited by the restriction of existing methods, and (iii) has an objective function that can be solved efficiently. Based on a simulation example and the analysis of two industrial datasets, the paper highlights that the regularized estimation technique outperforms existing work and shows how to embed this technique within an advanced process analytics framework for advanced process control, optimization and general process diagnostics.

Original languageBritish English
Article number105060
JournalControl Engineering Practice
StatePublished - Apr 2022


  • Big data
  • Error-in-variable models
  • Parameter estimation
  • Process data analytics
  • Regularization
  • Source signal extraction


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