Theoretical Limitations of Allan Variance-based Regression for Time Series Model Estimation

Stephane Guerrier, Roberto Molinari, Yannick Stebler

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


This letter formally proves the statistical inconsistency of the Allan variance-based estimation of latent (composite) model parameters. This issue has not been sufficiently investigated and highlighted since it is a technique that is still being widely used in practice, especially within the engineering domain. Indeed, among others, this method is frequently used for inertial sensor calibration, which often deals with latent time series models and practitioners in these domains are often unaware of its limitations. To prove the inconsistency of this method, we first provide a formal definition and subsequently deliver its theoretical properties, highlighting its limitations by comparing it with another statistically sound method.

Original languageEnglish (US)
Article number7433406
Pages (from-to)597-601
Number of pages5
JournalIEEE Signal Processing Letters
Issue number5
StatePublished - May 2016

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


Dive into the research topics of 'Theoretical Limitations of Allan Variance-based Regression for Time Series Model Estimation'. Together they form a unique fingerprint.

Cite this