|
Título:
|
A generalized Q–Q plot for longitudinal data
|
|
Autores:
|
Pardo Llorente, María del Carmen ;
Alonso Sanz, Rosa
|
|
Tipo de documento:
|
texto impreso
|
|
Editorial:
|
Routledge Journals, Taylor & Francis, 2012
|
|
Dimensiones:
|
application/pdf
|
|
Nota general:
|
info:eu-repo/semantics/restrictedAccess
|
|
Idiomas:
|
|
|
Palabras clave:
|
Estado = Publicado
,
Materia = Ciencias: Matemáticas: Estadística aplicada
,
Tipo = Artículo
|
|
Resumen:
|
Most biomedical research is carried out using longitudinal studies. The method of generalized estimating equations (GEEs) introduced by Liang and Zeger [Longitudinal data analysis using generalized linear models, Biometrika 73 (1986), pp. 13-22] and Zeger and Liang [Longitudinal data analysis for discrete and continuous outcomes, Biometrics 42 (1986), pp. 121-130] has become a standard method for analyzing non-normal longitudinal data. Since then, a large variety of GEEs have been proposed. However, the model diagnostic problem has not been explored intensively. Oh et al. [Modeldiagnostic plots for repeated measures data using the generalized estimating equations approach, Comput. Statist. Data Anal. 53 (2008), pp. 222-232] proposed residual plots based on the quantile-quantile (Q-Q) plots of the chi(2)-distribution for repeated-measures data using the GEE methodology. They considered the Pearson, Anscombe and deviance residuals. In this work, we propose to extend this graphical diagnostic using a generalized residual. A simulation study is presented as well as two examples illustrating the proposed generalized Q-Q plots.
|
|
En línea:
|
https://eprints.ucm.es/id/eprint/17432/1/PardoCarmen24.pdf
|
