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首页> 外文期刊>Ecological research >Detecting and cleaning outliers for robust estimation of variogram models in insect count data
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Detecting and cleaning outliers for robust estimation of variogram models in insect count data

机译：检测和清除异常值以可靠估计昆虫计数数据中的变异函数模型

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摘要

Outlier detection and cleaning procedures were evaluated to estimate mathematical restricted variogram models with discrete insect population count data. Because variogram modeling is significantly affected by outliers, methods to detect and clean outliers from data sets are critical for proper variogram modeling. In this study, we examined spatial data in the form of discrete measurements of insect counts on a rectangular grid. Two well-known insect pest population data were analyzed; one data set was the western flower thrips, Frankliniella occidentalis (Pergande) on greenhouse cucumbers and the other was the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) on greenhouse cherry tomatoes. A spatial additive outlier model was constructed to detect outliers in both the isolated and patchy spatial distributions of outliers, and the outliers were cleaned with the neighboring median cleaner. To analyze the effect of outliers, we compared the relative nugget effects of data cleaned of outliers and data still containing outliers after transformation. In addition, the correlation coefficients between the actual and predicted values were compared using the leave-one-out cross-validation method with data cleaned of outliers and non-cleaned data after unbiased back transformation. The outlier detection and cleaning procedure improved geostatistical analysis, particularly by reducing the nugget effect, which greatly impacts the prediction variance of kriging. Consequently, the outlier detection and cleaning procedures used here improved the results of geostatistical analysis with highly skewed and extremely fluctuating data, such as insect counts.
机译：对异常值检测和清洁程序进行了评估，以估计具有离散昆虫种群计数数据的数学受限变异函数模型。由于方差图建模受异常值显着影响，因此从数据集中检测和清除异常值的方法对于正确的方差图建模至关重要。在这项研究中，我们以矩形网格上昆虫计数的离散测量形式检查了空间数据。分析了两个著名的害虫种群数据；一个数据集是在温室黄瓜上的西花蓟马，西洋蓟花（Pergande），另一个是在温室樱桃番茄上的温室粉虱，粉虱Trialeurodes vaporariorum（Westwood）。构建了空间相加离群值模型，以检测离群值的孤立和零散空间分布中的离群值，并使用相邻的中值清洁器对离群值进行清理。为了分析离群值的影响，我们比较了清除离群值的数据和转换后仍包含离群值的数据的相对块效应。此外，使用留一法交叉验证方法将实际值和预测值之间的相关系数与无偏向反变换后的离群值清除数据和未清除数据进行比较。离群值检测和清理程序改善了地统计分析，特别是通过减少了金块效应，从而极大地影响了克里金法的预测方差。因此，此处使用的异常值检测和清理程序通过高度偏斜和极不稳定的数据（例如昆虫数量）改善了地统计分析的结果。

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  • 来源
    《Ecological research》 |2012年第1期|p.1-13|共13页
  • 作者

    Jung-Joon Park; Key-H Shin; Joon-Ho Lee; Sung Eun Lee; Woo-Kyun Lee; Kijong Cho;

  • 作者单位

    Institute of Life Science and Natural Resources, Korea University, Anam-dong, Sungbuk-ku, Seoul 136-701, Korea;

    Department of Statistics, Hankuk University of Foreign Studies, Yongin 449-791, Korea;

    Entomology Program, Department of Agricultural Biotechnology, Seoul National University, Seoul 151-921, Korea;

    Nanotoxtech Inc, #906, Gyeonggi Technopark, Ansan 426-901, Korea;

    Division of Environmental Science and Ecological Engineering, Korea University, l-5ka Anam-dong, Sungbuk-ku, Seoul 136-701, Korea;

    Division of Environmental Science and Ecological Engineering, Korea University, l-5ka Anam-dong, Sungbuk-ku, Seoul 136-701, Korea;

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  • 正文语种 eng
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  • 关键词

    variogram models; spatial additive model; outlier cleaner; western flower thrips; greenhouse whitefly; box-cox transformation;

    机译：变异函数模型;空间加性模型离群值清理器;西部花蓟马温室粉虱;Box-cox转换;

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