Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

8-2018

Journal or Book Title

Sankhya A

Volume

80

Issue

2

First Page

280

Last Page

300

DOI

10.1007/s13171-018-0129-7

Abstract

In spatial statistics, a common method for prediction over a Gaussian random field (GRF) is maximum likelihood estimation combined with kriging. For massive data sets, kriging is computationally intensive, both in terms of CPU time and memory, and so fixed rank kriging has been proposed as a solution. The method however still involves operations on large matrices, so we develop an alteration to this method by utilizing the approximations made in fixed rank kriging combined with restricted maximum likelihood estimation and sparse matrix methodology. Experiments show that our methodology can provide additional gains in computational efficiency over fixed-rank kriging without loss of accuracy in prediction. The methodology is applied to climate data archived by the United States National Climate Data Center, with very good results.

Comments

This is a post-peer-review, pre-copyedit version of an article published in Sankhya A. The final authenticated version is available online at DOI: 10.1007/s13171-018-0129-7.

Copyright Owner

Indian Statistical Institute

Language

en

File Format

application/pdf

Available for download on Thursday, August 01, 2019

Published Version

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