Mezclas de Procesos Gaussianos Jerárquicos Bayesianos para Análisis de Regresión
Bayesian Hierarchical Gaussian Process Mixtures for Regression Analysis
Abstract (en)
This paper essentially considers Gaussian process (GP) methods for regression analysis in longitudinal studies. The model is indeed a reasonable choice for description and prediction of phenomena involving repeated measurements in which there is evidence of heterogeneity among batches of measurements. First, we provide all the theoretical and practical details behind our modeling strategy. Then, we study the main properties of the model using simulated data. And finally, we apply analyze an AIDS clinical study developed by the AIDS Clinical Trials Group.
Abstract (es)
Este documento considera métodos de proceso gaussiano (GP) para el análisis de regresión en estudios longitudinales. El modelo es una elección razonable para la descripción y predicción de fenómenos implican mediciones repetidas en las que hay evidencia de heterogeneidad entre lotes de mediciones. Primero, proporcionamos todos los detalles teóricos y prácticos detrás de nuestra estrategia de modelado. Luego, estudiamos las principales propiedades del modelo utilizando datos simulados. Y finalmente, aplicamos y analizamos un estudio clínico sobre el SIDA desarrollado por el Grupo de Ensayos clínicos sobre el SIDA.
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