Robust multiscale estimation of time-average variance for time series segmentation
نویسندگان
چکیده
There exist several methods developed for the canonical change point problem of detecting multiple mean shifts, which search changes over sections data at scales. In such methods, estimation noise level is often required in order to distinguish genuine from random fluctuations due noise. When serial dependence present, using a single estimator may not be appropriate. Instead, it proposed adopt scale-dependent time-average variance constant that depends on length section consideration, gauge therein. Accordingly, an robust presence shifts developed. The consistency shown under general assumptions permitting heavy-tailedness, and its use with two widely adopted segmentation algorithms, moving sum wild binary procedures, discussed. performance illustrated through extensive simulation studies applications house price index air quality sets.
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2023
ISSN: ['0167-9473', '1872-7352']
DOI: https://doi.org/10.1016/j.csda.2022.107648