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Activity Number:
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89
- Nonparametric Methods for Modern Data
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Type:
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Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #317665
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Title:
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A General and Optimal Difference-Based Method for Variance Estimation in Time Series
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Author(s):
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Kin Wai Chan*
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Companies:
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The Chinese University of Hong Kong
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Keywords:
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change point detection;
non-linear time series;
optimal bandwidth selection;
trend inference;
variate difference method
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Abstract:
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Difference-based statistics are asymptotically invariant to arbitrary mean structures. They are natural building blocks for constructing estimators and test statistics. In this talk, we present a general framework for constructing variance estimators based on observations that are masked by serial dependence structures and time-varying mean structures. The proposed class of estimators is general enough to cover many existing estimators. Necessary and sufficient conditions for consistency are investigated. The first asymptotically optimal estimator is derived. Our proposed estimator is theoretically proven to be invariant to arbitrary mean structures, which may include trends and a possibly divergent number of discontinuities.
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Authors who are presenting talks have a * after their name.
