COMPARATIVE PERFORMANCE OF INDICATOR ALGORITHMS FOR MODELING CONDITIONAL-PROBABILITY DISTRIBUTION-FUNCTIONS
COMPARATIVE PERFORMANCE OF INDICATOR ALGORITHMS FOR MODELING CONDITIONAL-PROBABILITY DISTRIBUTION-FUNCTIONS
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DOI：
10.1007/bf02089230
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发表时间：
1994-04-01
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影响因子：
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通讯作者：
GOOVAERTS, P
中科院分区：
文献类型：
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作者：
GOOVAERTS, P
比较了条件累积分布函数(CCDF)的四种建模算法(全指标协同克立格法、相邻截止指标协同克立格法、多指标协同克立格法、中值指标克立格法)的性能。后三种算法是理论上更好的完全指标协克里格法的近似，因为它们忽略了某些指标变量之间的互协方差，或者它们认为所有协方差与同一函数成比例。使用包括2649个测量表土铜和钴的地点的参考土壤数据集来评估比较性能。在所有的实际应用中，指标协同克里格法并不比其他更简单的算法执行得更好，这些算法需要较少的变异函数建模工作量和较小的计算量。此外，发现协同克立格算法的顺序关系偏差数目更多，特别是当施加对克立格权重的约束时。
This paper compares the performance of four algorithms (full indicator cokriging, adjacent cutoffs indicator cokriging, multiple indicator kriging, median indicator kriging) for modeling conditional cumulative distribution functions (ccdf). The latter three algorithm are approximations to the theoretically better full indicator cokriging in the sense that they disregard cross-covariances between some indicator variables or they consider that all covariances are proportional to the same function. Comparative performance is assessed using a reference soil data set that includes 2649 locations at which both topsoil copper and cobalt were measured. For all practical purposes, indicator cokriging does not perform better than the other simpler algorithms which involve less variogram modeling effort and smaller computational cost. Furthermore, the number of order relation deviations is found to be higher for cokriging algorithms, especially when constraints on the kriging weights are applied.