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Activity Number:
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352
- Small Area Estimation, Analysis of Complex Sample Survey Data, and New Advances for Health Surveys
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Survey Research Methods Section
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Abstract #318036
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Title:
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Linear Quantile Mixed Models Under Informative Cluster Sampling
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Author(s):
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Sixia Chen and Daniel E Zhao and Chao Xu*
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Companies:
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University of Oklahoma Health Sciences Center and OUHSC Hudson College of Public Health and University of Oklahoma Health Sciences Center
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Keywords:
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Cluster sample;
Informative sampling;
Mixed model;
Quantile regression
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Abstract:
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Quantile regression model has been shown to be effective in the analysis with outliers or skewed distribution. Quantile mixed models with informative cluster sampling have not been well studied in previous literature. In this paper, by using the sampling distribution of estimated random effects, we propose a novel Monte Carlo expectation–maximization (EM) algorithm for estimation of linear quantile mixed models under informative cluster sampling design. Our proposed method is computationally more efficient than existing methods. Both simulation study and real application show the advantage of our proposed method compared with some other existing methods.
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Authors who are presenting talks have a * after their name.
