Elmanani Simamora, Subanar, Sri Haryatmi Kartiko
Plug-in kriging variance underestimates true kriging variance. This underestimator happens because kriging plug-in predictor ignores the randomness of errors or uncertainty of outputs in the locations of observed data. The correct kriging variance is proposed using semiparametric bootstrapping procedure. The simulation result for increasing observed I/O data location shows three properties, which are: (i) the values of generic estimation of kriging variance, semiparametric bootstrapping kriging variance, is always bigger than plug-in kriging variance, (ii) the decline of the estimation values of both estimators tends to be zero, and (iii) conditional number of correlation matrix increases, enabling matrix in ill condition. One of the causes of ill condition is rounding error in expensive computation, decreasing the accuracy of the estimation, even causing loss of the solution of kriging equation system. With the assumption that computation aspect is ignored, ill condition, it can be analytically shown that the asymptotic property of both estimators, i.e: plug-in kriging variance and generic estimator, are consistent to zero. © 2015 Elmanani Simamora, Subanar and Sri Haryatmi Kartiko.
Department of Mathematics, State University of Medan, North Sumatera, Indonesia; Department of Mathematics, Gadjah Mada University, Yogyakarta, Indonesia; Department of Mathematics, Gadjah Mada University, Yogyakarta, Indonesia
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock