Artículo
Robust estimation for spatial autoregressive processes based on bounded innovation propagation representations
Fecha de publicación:
09/2019
Editorial:
Springer Heidelberg
Revista:
Computational Statistics (zeitschrift)
ISSN:
0943-4062
e-ISSN:
1613-9658
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Robust methods have been a successful approach for dealing with contamination and noise in the context of spatial statistics and, in particular, in image processing. In this paper, we introduce a new robust method for spatial autoregressive models. Our method, called BMM-2D, relies on representing a two-dimensional autoregressive process with an auxiliary model to attenuate the effect of contamination (outliers). We compare the performance of our method with existing robust estimators and the least squares estimator via a comprehensive Monte Carlo simulation study, which considers different levels of replacement contamination and window sizes. The results show that the new estimator is superior to the other estimators, both in accuracy and precision. An application to image filtering highlights the findings and illustrates how the estimator works in practical applications.
Palabras clave:
AR-2D MODELS
,
ROBUST ESTIMATORS
,
IMAGE PROCESSING
,
SPACIAL MODELS
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Licencia
Identificadores
Colecciones
Articulos(CIEM)
Articulos de CENT.INV.Y ESTUDIOS DE MATEMATICA DE CORDOBA(P)
Articulos de CENT.INV.Y ESTUDIOS DE MATEMATICA DE CORDOBA(P)
Citación
Britos, Grisel Maribel; Ojeda, Silvia María; Robust estimation for spatial autoregressive processes based on bounded innovation propagation representations; Springer Heidelberg; Computational Statistics (zeitschrift); 34; 3; 9-2019; 1315-1335
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