Centre for Paediatric Epidemiology and Biostatistics
Institute of Child Health, University College London
Departmental Seminar
Linear Quantile Mixed Models
Dr Marco Geraci
MRC Centre of Epidemiology for Child Health
University College London
Wednesday 26th January 2011, 2-3pm
Room B, Wellcome Trust Building
UCL Institute of Child Health
30 Guilford Street, London, WC1N 1EH
ABSTRACT
Dependent data arise in many studies. For example, children with the
same parents or living in neighbouring
geographic areas tend to be more alike in many characteristics than
individuals chosen at random from the
population at large; observations taken repeatedly on the same
individual are likely to be more similar than
observations from different individuals. Frequently adopted sampling
designs, such as cluster, multilevel,
spatial, and repeated measures (or longitudinal or panel), may induce
this dependence, which the analysis
of the data needs to take into due account. Particularly challenging
problems need to be tackled when the
target of the inference extends beyond the mean and includes one or
multiple specified quantiles of the
outcome distribution conditional on a set of predictors. In this talk I
will describe a class of conditional
quantile regression models for continuous responses where random effects
are included along with
fixed-coefficient predictors to account for within-groups dependence in
the context of clustered data analysis.
I will also show some preliminary results based on the use of functions
written as part of the ongoing project 'lqmm',
an R package for quantile regression with random effects.
--
Mario Cortina Borja; [log in to unmask]
Centre for Paediatric Epidemiology& Biostatistics
UCL Institute of Child Health, London WC1N 1EH, UK
Phone +44(0)20 7905 2113 Fax +44(0)20 7905 2381
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