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Activity Number:
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65
- Forecasting and Time Series Estimation
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #311160
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Title:
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Efficient Non-Parametric Spectral Density Estimation with Randomly Censored Data
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Author(s):
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Jiaju Wu* and Sam Efromovich
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Companies:
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University of Texas at Dallas and University of Texas at Dallas
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Keywords:
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Spectral Density;
Time Series;
Right Censoring;
Survival Analysis;
Non-parametric
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Abstract:
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Spectral Density estimation is a well known problem for a directly observed time series. The literature on spectral density estimation for a right-censored time series is next to none. A data-driven spectral density estimator for a right censored time series is suggested. This estimator adapts to unknown smoothness of the spectral density and unknown distribution of a censored random variable. Asymptotic upper bound of the mean integrated squared error (MISE) of the proposed estimator is obtained. The estimator is studied via simulated examples.
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Authors who are presenting talks have a * after their name.
