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dc.contributor.authorFernandez-Val, Ivanen_US
dc.contributor.authorFrumento, Paoloen_US
dc.contributor.authorBottai, Matteoen_US
dc.date2021-01-06
dc.date.accessioned2021-03-23T13:42:23Z
dc.date.available2021-03-23T13:42:23Z
dc.date.issued2020
dc.identifier.citationIvan Fernandez-Val, Paolo Frumento, Matteo Bottai. "Parametric Modeling of Quantile Regression Coefficient Functions with Longitudinal Data." Journal of the American Statistical Association, https://arxiv.org/abs/2006.00160v1
dc.identifier.issn0162-1459
dc.identifier.urihttps://hdl.handle.net/2144/42306
dc.description.abstractIn ordinary quantile regression, quantiles of different order are estimated one at a time. An alternative approach, which is referred to as quantile regression coefficients modeling (qrcm), is to model quantile regression coefficients as parametric functions of the order of the quantile. In this paper, we describe how the qrcm paradigm can be applied to longitudinal data. We introduce a two-level quantile function, in which two different quantile regression models are used to describe the (conditional) distribution of the within-subject response and that of the individual effects. We propose a novel type of penalized fixed-effects estimator, and discuss its advantages over standard methods based on ℓ1 and ℓ2 penalization. We provide model identifiability conditions, derive asymptotic properties, describe goodness-of-fit measures and model selection criteria, present simulation results, and discuss an application. The proposed method has been implemented in the R package qrcm.en_US
dc.description.urihttps://arxiv.org/abs/2006.00160
dc.language.isoen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofJournal of the American Statistical Association
dc.subjectStatisticsen_US
dc.subjectEconometricsen_US
dc.subjectDemographyen_US
dc.subjectStatistics & probabilityen_US
dc.subjectLongitudinal quantile regressionen_US
dc.subjectTwo-level quantile functionen_US
dc.subjectParametric quantile functionen_US
dc.subjectPenalized fixed-effectsen_US
dc.subjectR package qrcmen_US
dc.titleParametric modeling of quantile regression coefficient functions with longitudinal dataen_US
dc.typeArticleen_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Economicsen_US
pubs.publication-statusAccepteden_US
dc.description.oaversionAccepted manuscript
dc.identifier.mycv590616


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