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Maximum‐likelihood estimation for multivariate spatial linear coregionalization models
Author(s) -
Zhang Hao
Publication year - 2007
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.807
Subject(s) - multivariate statistics , maximum likelihood , estimation , computer science , expectation–maximization algorithm , linear model , maximum likelihood sequence estimation , estimation theory , statistics , class (philosophy) , multivariate analysis , restricted maximum likelihood , econometrics , mathematical optimization , mathematics , algorithm , artificial intelligence , management , economics
Abstract A multivariate spatial linear coregionalization model is considered that incorporates the Matérn class of covariograms. An EM algorithm is developed for maximum‐likelihood estimation that has a few desirable properties and is capable of handling high‐dimensional data. Most estimates in the EM algorithm are updated through closed form expressions and these estimates automatically satisfy necessary constraints. The model and algorithm are illustrated through a real example. Copyright © 2006 John Wiley & Sons, Ltd.