Sequential change-point detection in time series models based on pairwise likelihood
Publication in refereed journal

香港中文大學研究人員
替代計量分析
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其它資訊
摘要The paper proposes a sequential monitoring scheme for detecting changes in parameter values for general time series models using pairwise likelihood. Under this scheme, a change-point is declared when the cumulative sum of the first derivatives of pairwise likelihood exceeds a certain boundary function. The scheme is shown to have asymptotically zero Type II error with a prescribed level of Type I error. With the use of pairwise likelihood, the scheme is applicable to many complicated time series models in a computationally efficient manner. For example, the scheme covers time series models involving latent processes, such as stochastic volatility models and Poisson regression models with log link function.
著者LEUNG Sze Him, NG Wai Leong
期刊名稱Statistica Sinica
出版年份2017
卷號27
期次2
頁次575 - 606
國際標準期刊號1017-0405
電子國際標準期刊號1996-8507
語言美式英語

上次更新時間 2021-17-10 於 00:05