This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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551
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Type:
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Invited
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #305998 |
Title:
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Nonparametric and Semiparametric M-Quantile Inference for Longitudinal Data
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Author(s):
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Nikos Tzavidis and Nicola Salvati and Hukum Chandra and Raymond Chambers*+
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Companies:
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CSSR and DSMAE and IASRI and CSSM
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Address:
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University of Wollongong, Wollongong, International, , Australia
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Keywords:
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M-quantile regression ;
Quantile regression ;
Spline regression ;
Mixed models ;
Hierarchical data ;
Random effects
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Abstract:
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M-quantile regression is used to model the M-quantiles of the conditional distribution of an outcome variable given a set of covariates. An M-quantile random effects model allows one to account for data with hierarchical structure, e.g. longitudinal data, when modelling these M-quantiles. When the functional form of the relationship between the response variable and the covariates is unknown or is complicated, an approach based on use of a penalized spline approximation can offer signi?cant advantages compared with one based on a linear model. In this paper we extend the M-quantile random effects model to nonparametric regression, in the sense that the M-quantile regression functions are estimated from the data using penalised spline approximations. Maximum likelihood estimation for this model is described and then evaluated via simulation studies as well as by a real world application.
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Authors who are presenting talks have a * after their name.
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