Importance of orthogonalization algorithm in modeling conditional distributions by orthogonal transformed indicator methods

Tercan A. E.

MATHEMATICAL GEOLOGY, cilt.31, sa.2, ss.155-173, 1999 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31 Sayı: 2
  • Basım Tarihi: 1999
  • Doi Numarası: 10.1023/a:1003638700879
  • Dergi Adı: MATHEMATICAL GEOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.155-173
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.