期刊
BERNOULLI
卷 6, 期 5, 页码 845-869出版社
INT STATISTICAL INST
DOI: 10.2307/3318759
关键词
detection of change-points; long range dependence; maximal inequality; nonparametric spectral estimation; penalized minimum contrast estimate; quadratic forms; Whittle likelihood
We consider the problem of detecting an unknown number of change-points in the spectrum of a second-order stationary random process. To reach this goal, some maximal inequalities for quadratic forms are first given under very weak assumptions. In a parametric framework, and when the number of changes is known, the change-point instants and the parameter vector are estimated using the Whittle pseudo-likelihood of the observations. A penalized minimum contrast estimate is proposed when the number of changes is unknown. The statistical properties of these estimates hold for strongly mixing and also long-range dependent processes. Estimation in a nonparametric framework is also considered, by using the spectral measure function. We conclude with an application to electroencephalogram analysis.
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