Change detection in autoregressive time series

By Edit Gombay. 2008

121 citations recorded by the source corpus.

Autoregressive time series models of order p have p+2 parameters, the mean, the variance of the white noise and the p autoregressive parameters. Change in any of these over time is a sign of disturbance that is important to detect. The methods of this paper can test for change in any one of these p+2 parameters separately, or in any collection of them. They are available in forms that make one-sided tests possible, furthermore, they can be used to test for a temporary change. The test statistics are based on the efficient score vector. The large sample properties of the change-point estimator are also explored.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/002fbea4ad322eee97341f4ebc707c02d46fd4a4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Lajos Horv'ath
2. István Berkes
3. Idris Arthur Eckley
4. Paul Fearnhead
5. Rebecca Killick

In Statistics and Probability

1. Lajos Horv'ath
2. István Berkes
3. Paul Fearnhead
4. Idris Arthur Eckley
5. Rebecca Killick
6. Edit Gombay
7. Alexander Aue
8. Gregory Rice

Citations in the corpus

• Optimal detection of changepoints with a linear computational cost (2011, methodology): “For the data we consider (Figure 4 (2006), Huskova et al. (2007) and Gombay (2008).”
• Testing for Change Points in Time Series (2010, unknown): “KEY WORDS: CUSUM; Invariance principle; Self-normalization.”
• Testing for changes in the covariance structure of linear processes (2009, methodology): “Using a test for change in variance for the autoregressive model of Gombay (2008) reduces the two-sided p-value to 0.”