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Activity Number:
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132
- Functional Data and Time Series
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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Abstract #304611
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Title:
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Robust M-Estimation for Partially Observed Functional Data
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Author(s):
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Yeonjoo Park* and Xiaohui Chen and Douglas Simpson
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Companies:
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University of Texas at San Antonio and University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
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Keywords:
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Bootstrap;
Functional central limit theorem;
Irregular functional data;
M-estimates of location;
Robustness;
Trend inference
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Abstract:
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Irregular functional data in which densely sampled curves are observed over different ranges pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in many applications. This paper investigates a class of robust M-estimators for partially observed functional data. To derive asymptotic properties under irregular structure using a missing data framework. We derive asymptotic normality of functional M-estimator under the proposed framework and show root-n rates of convergence. Furthermore, we propose a class of functional trend tests to find significant directions in the trend of location. For the implementation of the inferential test, we adopt a joint bootstrap approach. The performance is demonstrated in simulations and in application to data from quantitative ultrasound analysis.
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Authors who are presenting talks have a * after their name.
