Skip to main content

Approximating likelihoods for large spatial data sets

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

Summary. 

Likelihood methods are often difficult to use with large, irregularly sited spatial data sets, owing to the computational burden. Even for Gaussian models, exact calculations of the likelihood for n observations require O(n3) operations. Since any joint density can be written as a product of conditional densities based on some ordering of the observations, one way to lessen the computations is to condition on only some of the ‘past’ observations when computing the conditional densities. We show how this approach can be adapted to approximate the restricted likelihood and we demonstrate how an estimating equations approach allows us to judge the efficacy of the resulting approximation. Previous work has suggested conditioning on those past observations that are closest to the observation whose conditional density we are approximating. Through theoretical, numerical and practical examples, we show that there can often be considerable benefit in conditioning on some distant observations as well.

Keywords: Chlorophyll fluorescence; Estimating equations; Restricted maximum likelihood; Variogram estimation

Document Type: Research Article

DOI: http://dx.doi.org/10.1046/j.1369-7412.2003.05512.x

Publication date: April 1, 2004

bpl/rssb/2004/00000066/00000002/art00001
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more