Robust recursive estimation for correlated observations

Irwin Guttman; Dennis K.J. Lin

doi:10.1016/0167-7152(94)00098-S

Robust recursive estimation for correlated observations

Irwin Guttman, Dennis K.J. Lin

Statistics

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	79-92
Number of pages	14
Journal	Statistics and Probability Letters
Volume	23
Issue number	1
DOIs	https://doi.org/10.1016/0167-7152(94)00098-S
State	Published - Apr 1995

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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