摘要翻译：
有各种方法来分析不同种类的数据集。当数据基于各自的位置相互依赖时，定义空间数据。样条和克立格是空间数据插值和预测的两种方法。在一定条件下，这些方法是等价的，但在实践中表现出不同的行为。只有在被选为采样点位置的某些位置才能观测到大量的数据，因此，预测其他位置的数据值是非常重要的。本文首先介绍了样条函数和克里格方法之间的联系，然后对流行病学二维真实数据集，分别在经度和纬度上观测数据，并对这两种方法的性能进行了研究。对这些性能的比较表明，对于该数据集，Kriging方法比样条方法具有更好的性能。
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英文标题：
《Comparison of Spline with Kriging in an Epidemiological Problem》
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作者：
Roshanak Alimohammadi
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  There are various methods to analyze different kinds of data sets. Spatial data is defined when data is dependent on each other based on their respective locations. Spline and Kriging are two methods for interpolating and predicting spatial data. Under certain conditions, these methods are equivalent, but in practice they show different behaviors. Amount of data can be observed only at some positions that are chosen as positions of sample points, therefore, prediction of data values in other positions is important. In this paper, the link between Spline and Kriging methods is described, then for an epidemiological two dimensional real data set, data is observed in geological longitude and in latitude dimensions, and behavior of these methods are investigated. Comparison of these performances show that for this data set, Kriging method has a better performance than Spline method.
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PDF链接：
https://arxiv.org/pdf/711.4883