Functional calibration estimation by the maximum entropy on the mean principle

ABSTRACT: We extend the problem of obtaining an estimator for the finite population mean parameter incorporating complete auxiliary information through calibration estimation in survey sampling but considering a functional data framework. The functional calibration sampling weights of the estimator are obtained by matching the calibration estimation problem with the maximum entropy on the mean principle. In particular, the calibration estimation is viewed as an infinite dimensional linear inverse problem following the structure of the maximum entropy on the mean approach. We give a precise theoretical setting and estimate the functional calibration weights assuming, as prior measures, the centered Gaussian and compound Poisson random measures. Additionally, through a simple simulation study, we show that our functional calibration estimator improves its accuracy compared with the Horvitz-Thompson estimator.

• Autores: Gallón Gómez, Santiago Alejandro; Loubes, Jean Michel; Gamboa, Fabrice
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2015
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/39626
• Acceso en línea: https://hdl.handle.net/10495/39626
• Palabra clave: Entropía; Entropy; Auxiliary information; Functional calibration weights; Functional data; Infinite dimensional linear inverse problems; Survey sampling
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/2.5/co/