학술논문

Missing-Data Nonparametric Coherency Estimation
Document Type
Periodical
Author
Source
IEEE Signal Processing Letters IEEE Signal Process. Lett. Signal Processing Letters, IEEE. 28:1704-1708 2021
Subject
Signal Processing and Analysis
Computing and Processing
Communication, Networking and Broadcast Technologies
Coherence
Time series analysis
Frequency estimation
Smoothing methods
Recruitment
Oscillators
Indexes
Lomb-Scargle periodogram
missing data problem
multivariate time series
multitaper
power spectrum
Language
ISSN
1070-9908
1558-2361
Abstract
Chave recently proposed an estimator for multitaper spectral density where the time series contains missing values. In this article we generalize this technique to a multitaper estimator of coherence and phase and show that one can also obtain bootstrapped confidence intervals. We give two examples. The first is a toy example in which the true coherence is known. In the second example we show that the multitaper missing-data coherence estimator computed on real data with a single gap comprising 11% of the data outperforms the Daniell-smoothed coherence estimator where there are no gaps. The case where the two time series have different missing indices is also discussed.