Imputing censored data with desirable spatial covariance function properties using simulated annealing
Imputing censored data with desirable spatial covariance function properties using simulated annealing
复制标题
使用模拟退火将删失数据输入具有所需的空间协方差函数属性
DOI：
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复制
发表时间：
2012
影响因子：
2.9
通讯作者：
G. Passarella
中科院分区：
文献类型：
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作者：
Luigi Sedda;P. Atkinson;E. Barca;G. Passarella
当小于检测限的测量值报告为未检出时，数据被称为删失数据。在土壤科学研究中，不记录低于检测限的值是很常见的，尽管受审查影响的建模数据可能会有问题。本文开发和测试了一个修改版本的空间模拟退火，称为模拟退火变差函数和直方图形式，绘制值的截尾点给定一组混合的观察和截尾数据。该算法旨在最大限度地提高实验和理论变异函数之间的拟合优度（通过允许其参数的变化），同时将插补值约束为目标直方图形式。在实践中，通过将可用数据（间隔和精确观测值）转换为分位数并拟合合理分布来估计实验直方图。数据的理论分布被用来约束变差函数拟合。建议的模拟退火方法的目的是找到最佳的空间安排的值，在变差函数和直方图拟合和克里金预测的最低误差。在已知删失点值的模拟数据集上评估了该方法的精度，并与空间模拟退火算法进行了比较。根据所获得的结果，模拟退火变异函数和直方图形式（SAVH）的方法可以推荐作为一个有用的工具，分析空间分布的数据与删失。
When measurements of values that are less than the limit of detection are reported as not detected, the data are referred to as censored. The non-recording of values below the limit of detection is common in soil science research although modelling data affected by censoring can be problematic. This paper develops and tests a modified version of Spatial Simulated Annealing, called Simulated Annealing by Variogram and Histogram form, for drawing values for censored points given a mixed set of observed and censored data. The algorithm aims to maximise the goodness of fitting between the experimental and theoretical variograms (by allowing variation in its parameters) while the imputed values are constrained to a target histogram form. In practice, the experimental histogram is estimated by transforming the available data (interval and exact observations) to quantiles and fitting a plausible distribution. The theoretical distribution of the data is used to constrain the variogram fitting. The proposed simulated annealing method is designed to find the optimal spatial arrangement of values, given by the lowest errors in variogram and histogram fitting and kriging prediction. The accuracy of the method proposed is assessed on a simulated data set in which the censored point values are known and compared with the Spatial Simulated Annealing algorithm. According to the results obtained, the Simulated Annealing by Variogram and Histogram form (SAVH) approach can be recommended as a useful tool for the analysis of spatially distributed data with censoring.