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Activity Number:
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346
- Recent Advances in Nonparametric Statistical Methods
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #329513
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Title:
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Nonparametric Estimation of Risk Tracking Indices for Longitudinal Studies
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Author(s):
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Xin Tian* and Colin O. Wu
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Companies:
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National Heart, Lung and Blood Institute and National Heart, Lung and Blood Institute, NIH
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Keywords:
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Dynamic tracking;
Longitudinal data;
Nonparametric estimation;
Rank-tracking index;
Time-varying distribution;
kernel estimation
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Abstract:
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Tracking a subject's risk factors or health status over time is an important objective in long-term epidemiology studies with repeated measurements. An important issue of time-trend tracking is to define appropriate statistical indices to quantitatively measure the tracking abilities of the targeted risk factors over time. We present a number of local and global statistical tracking indices based on the rank-tracking probabilities, which are derived from the conditional distribution functions, and propose a class of kernel based nonparametric estimation methods. Confidence intervals for the estimators of the tracking indices are constructed through a resampling subject bootstrap procedure. We demonstrate the application of the tracking indices using the body mass index and systolic blood pressure data from the Coronary Artery Risk Development in Young Adults (CARDIA) study. Statistic properties of the estimation methods and bootstrap inference are investigated through a simulation study and an asymptotic development.
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Authors who are presenting talks have a * after their name.
