Importance of orthogonalization algorithm in modeling conditional distributions by orthogonal transformed indicator methods
MATHEMATICAL GEOLOGY, vol.31, no.2, pp.155-173, 1999 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 31 Issue: 2
- Publication Date: 1999
- Doi Number: 10.1023/a:1003638700879
- Journal Name: MATHEMATICAL GEOLOGY
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.155-173
- Hacettepe University Affiliated: Yes
Abstract
The orthogonal transformed indicator approach to conditional cumulative distribution functions is based on the decomposition of the indicator variogram matrix as a matrix product. This paper explores the manner in which the decomposition algorithm affects the conditional cumulative distribution function as estimated by orthogonal transformed indicator kriging. Five decomposition algorithms are considered: spectral, Cholesky symmetric, Cholesky-spectral, and simultaneous decompositions. Impact of the algorithms on spatial orthogonality and order relations problems is examined and their performances together with indicator kriging are compared using a real dataset.