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Activity Number:
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234
- New Challenges in Statistical Learning and Inference for Complex Data
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section for Statistical Programmers and Analysts
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Abstract #320800
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Title:
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Nonparametric Comparison of Time Series via Quantile Periodograms
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Author(s):
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Lei Jin*
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Companies:
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Texas A&M University - Corpus Christi
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Keywords:
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autocovariance ;
non-stationary;
quantile periodogram;
robust;
time series
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Abstract:
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Motivated by real-world applications, new methods have been proposed to check if two or multiple non-stationary time series have the same dynamics. The proposed methods rely on quantile periodograms and do not need any specific parametric assumptions. The methods are developed and their asymptotic properties have been obtained, even when the autocovariance structures may not exist. The proposed methods are applicable to compare non-stationary time series which may be dependent on each other. A Monte Carlo simulation study illustrates the validity of the asymptotic results and the finite sample performance. The proposed methods have been applied to an analysis of non-stationary signals for damage detection.
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Authors who are presenting talks have a * after their name.
