Statistics and Its Interface

Volume 16 (2023)

Number 1

Special issue on recent developments in complex time series analysis – Part I

Guest editors: Robert T. Krafty (Emory Univ.), Guodong Li (Univ. of Hong Kong), Anatoly Zhigljavsky (Cardiff Univ.)

Detection of signals by Monte Carlo singular spectrum analysis: multiple testing

Pages: 147 – 157

DOI: https://dx.doi.org/10.4310/21-SII715

Author

Nina Golyandina (Department of Statistical Modeling, St. Petersburg State University, St. Petersburg, Russia)

Abstract

Detection of a signal in a noisy time series using Monte Carlo singular spectrum analysis (MC-SSA) is studied from the statistical viewpoint. The MC-SSA test consists of simultaneous testing of several hypotheses related to the presence of different frequencies. The multiple MC-SSA test procedure is constructed to control the family-wise error rate. The technique to control both the type I and the type II errors and also to compare criteria is proposed to study several versions of MC-SSA.

Keywords

singular spectrum analysis, time series, signal detection, multiple testing, family-wise error rate

2010 Mathematics Subject Classification

Primary 62G10, 94A12. Secondary 37M10, 60G35.

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The reported study was funded by RFBR, project number 20-01-00067.

Received 1 March 2021

Accepted 30 November 2021

Published 28 December 2022