Robust recursive estimation for correlated observations

Irwin Guttman, Dennis K.J. Lin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The Kalman filter is probably the most popular recursive estimation method. It is, however, known to be non-robust to spuriously generated observations. Much attention has been focused on finding the so-called robust recursive estimation under the assumption that the observations are independent. In this paper, we show that Lin and Guttman's robust recursive estimation scheme can be easily applied to the correlated observations. Examples when the noise follows an AR(2) process with/without outliers are given for illustration.

Original languageEnglish (US)
Pages (from-to)79-92
Number of pages14
JournalStatistics and Probability Letters
Issue number1
StatePublished - Apr 1995

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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