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On the Applicability of Bartlett Lewis Model: With Reference to Missing Data

机译：关于巴特利特·刘易斯模型的适用性：参考缺失数据

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The availability of a complete hourly rainfall dataset is required for hydrological applications, statistical modeling and forecasting of precipitation. The issues of missing data are of serious concern in rainfall modelling due to the problem in computing the autocorrelation when gaps of missing data are encountered in the pooled data. The present paper discusses the applicability of three methods of handling missing hourly data when the Bartlett Lewis rectangular pulses model is utilized: zero substitution, single imputation and multiple imputations. The three methods are applied to the hourly rainfall data from the Bukit Bendera rain gauge station, which consists of a complete nine year rainfall series. The methods are tested with different percentages of randomly generated missing rainfall values. The performance of the methods is studied in terms of the mean absolute deviation errors that are found during different monsoon periods. The findings indicate that the best method to address missing data when applying the Bartlett Lewis rectangular pulses model is the single imputation method.Keywords: Single Imputation; Multiple Imputations; Bartlett Lewis Rectangular Pulses Model2010 Mathematics Subject Classification: 91B70
机译：水文应用，统计建模和降水预报需要完整的每小时降雨数据集。由于在合并数据中遇到缺失数据的间隙时，在计算自相关时存在问题，因此降雨模型中的缺失数据问题引起了人们的严重关注。本文讨论了利用巴特利特·刘易斯矩形脉冲模型处理时空数据丢失的三种方法的适用性：零替代，单归和多归。这三种方法应用于来自Bukit Bendera雨量计站的每小时降雨数据，该数据包括一个完整的9年降雨序列。使用不同百分比的随机生成的缺失降雨值对方法进行了测试。根据在不同季风期间发现的平均绝对偏差误差来研究方法的性能。研究结果表明，在应用Bartlett Lewis矩形脉冲模型时，解决缺失数据的最佳方法是单一插补方法。多重插补; Bartlett Lewis矩形脉冲Model2010数学主题分类：91B70

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