Cokriging uses information on sever

Cokriging uses information on several variable types. The main variable of interest is Z1, and both autocorrelation for Z1 and cross-correlations between Z1 and all other variable types are used to make better predictions. It is appealing to use information from other variables to help make predictions, but it comes at a price. Cokriging requires much more estimation, including estimating the autocorrelation for each variable as well as all cross-correlations. Theoretically, you can do no worse than kriging because if there is no cross-correlation, you can fall back on autocorrelation for Z1. However, each time you estimate unknown autocorrelation parameters, you introduce more variability, so the gains in precision of the predictions may not be worth the extra effort.

ผลลัพธ์ (แอฟริกา) 1:

Cokriging gebruik inligting op verskeie veranderlike tipes. Die belangrikste veranderlike van belang is Z1, en beide outokorrelasie vir Z1 en kruis-korrelasies tussen Z1 en alle ander veranderlike tipes word gebruik om 'n beter voorspellings te maak. Dit is 'n beroep op inligting uit ander veranderlikes gebruik om te help om voorspellings nie, maar dit kom teen 'n prys. Cokriging verg baie meer skatting, insluitend die skatte van die outokorrelasie vir elke veranderlike, asook al kruis-korrelasies. Teoreties, kan jy nie erger as kriging doen, want as daar geen kruis-korrelasie, kan jy terug te val op outokorrelasie vir Z1. Maar elke keer as jy onbekend outokorrelasie parameters beraam, stel jy meer variasie, so die winste in akkuraatheid van die voorspellings kan nie die ekstra moeite werd wees.