Spatial Mallows model averaging for geostatistical models
- Autores: Jun Liao, Guohua Zou, Yan Gao
- Localización: Canadian Journal of Statistics = Revue Canadienne de Statistique, ISSN 0319-5724, Vol. 47, Nº. 3, 2019, págs. 336-351
- Idioma: inglés
- DOI: 10.1002/cjs.11497
- Enlaces
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Resumen
AbstractENTHIS LINK GOES TO A ENGLISH SECTIONFRTHIS LINK GOES TO A FRENCH SECTION Important progress has been made with model averaging methods over the past decades. For spatial data, however, the idea of model averaging has not been applied well. This article studies model averaging methods for the spatial geostatistical linear model. A spatial Mallows criterion is developed to choose weights for the model averaging estimator. The resulting estimator can achieve asymptotic optimality in terms of L2 loss. Simulation experiments reveal that our proposed estimator is superior to the model averaging estimator by the Mallows criterion developed for ordinary linear models [Hansen, 2007] and the model selection estimator using the corrected Akaike's information criterion, developed for geostatistical linear models [Hoeting et al., 2006]. The Canadian Journal of Statistics 47: 336–351; 2019 © 2019 Statistical Society of Canada
