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Abstract Details
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Activity Number:
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386
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Type:
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Invited
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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General Methodology
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| Abstract - #300184 |
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Title:
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A Confidence Corridor for Sparse Longitudinal Data Curves
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Author(s):
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Shuzhuan Zheng*+ and Lijian Yang and Wolfgang Karl Hardle
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Companies:
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Michigan State University and Michigan State University and Humboldt-University zu Berlin
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Address:
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Department of Statistics and Probability, East Lansing, MI, 48824,
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Keywords:
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Longitudinal data ;
Confidence band ;
Local linear estimator ;
Extreme value ;
Double sum ;
Strong approximation
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Abstract:
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Longitudinal data analysis is a central piece of statistics. The data are curves and they are observed at random locations. This makes the construction of a simultaneous confidence corridor (SCC) (confidence band) for the mean function a challenging task on both the theoretical and the practical side. Here we propose a method based on local linear smoothing that is implemented in the sparse (i.e., low number of nonzero coefficients) modelling situation. An SCC is constructed based on recent results obtained in applied probability theory. The precision and performance is demonstrated in a spectrum of simultaneous and applied to growth curve data. Technically speaking, our paper intensively uses recent insights into extreme value theory that are also employed to construct a shoal of confidence intervals (SCI).
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Authors who are presenting talks have a * after their name.
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