Change Detection Model for Serially Correlated Multivariate Data

Abstract

SUMMARY A method is presented for detecting change in biological multivariate stationary processes of a single subject after stimulation. A finite multivariate autoregression is fitted to pre-stimulus data using a step-wise procedure with tests of significance. The fit of the model is also checked by comparing the estimated spectra, phase, and coherence with fitted curves. The statistic which tests for change at a given time point is a quadratic form involving the one-step prediction error vector ancd the inverse of the one-step prediction error covariance matrix. Under the hypothesis of no change, and for a Gaussian process, these statistics have independent chi-square distributions. The technilque has been applied to the detection of change in the brain waves of two human newborn infant's following stimulation.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 33 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.