{"title":"kendallknight:肯德尔相关系数计算的高效实现","authors":"Mauricio Vargas Sepúlveda","doi":"arxiv-2408.09618","DOIUrl":null,"url":null,"abstract":"The kendallknight package introduces an efficient implementation of Kendall's\ncorrelation coefficient computation, significantly improving the processing\ntime for large datasets without sacrificing accuracy. The kendallknight\npackage, following Knight (1966) and posterior literature, reduces the\ncomputational complexity resulting in drastic reductions in computation time,\ntransforming operations that would take minutes or hours into milliseconds or\nminutes, while maintaining precision and correctly handling edge cases and\nerrors. The package is particularly advantageous in econometric and statistical\ncontexts where rapid and accurate calculation of Kendall's correlation\ncoefficient is desirable. Benchmarks demonstrate substantial performance gains\nover the base R implementation, especially for large datasets.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"157 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"kendallknight: Efficient Implementation of Kendall's Correlation Coefficient Computation\",\"authors\":\"Mauricio Vargas Sepúlveda\",\"doi\":\"arxiv-2408.09618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The kendallknight package introduces an efficient implementation of Kendall's\\ncorrelation coefficient computation, significantly improving the processing\\ntime for large datasets without sacrificing accuracy. The kendallknight\\npackage, following Knight (1966) and posterior literature, reduces the\\ncomputational complexity resulting in drastic reductions in computation time,\\ntransforming operations that would take minutes or hours into milliseconds or\\nminutes, while maintaining precision and correctly handling edge cases and\\nerrors. The package is particularly advantageous in econometric and statistical\\ncontexts where rapid and accurate calculation of Kendall's correlation\\ncoefficient is desirable. Benchmarks demonstrate substantial performance gains\\nover the base R implementation, especially for large datasets.\",\"PeriodicalId\":501293,\"journal\":{\"name\":\"arXiv - ECON - Econometrics\",\"volume\":\"157 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - ECON - Econometrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.09618\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - Econometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.09618","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
kendallknight 软件包引入了肯德尔相关系数计算的高效实现方法,在不牺牲准确性的前提下,显著改善了大型数据集的处理时间。kendallknight 软件包遵循 Knight (1966) 和后继文献,降低了计算复杂度,从而大幅减少了计算时间,将需要几分钟或几小时的操作转化为几毫秒或几分钟,同时保持了精度,并正确处理了边缘情况和错误。该软件包在计量经济学和统计领域尤其具有优势,因为这些领域需要快速、准确地计算肯德尔相关系数。基准测试表明,与基本 R 实现相比,该软件包的性能大幅提升,尤其是在大型数据集上。
kendallknight: Efficient Implementation of Kendall's Correlation Coefficient Computation
The kendallknight package introduces an efficient implementation of Kendall's
correlation coefficient computation, significantly improving the processing
time for large datasets without sacrificing accuracy. The kendallknight
package, following Knight (1966) and posterior literature, reduces the
computational complexity resulting in drastic reductions in computation time,
transforming operations that would take minutes or hours into milliseconds or
minutes, while maintaining precision and correctly handling edge cases and
errors. The package is particularly advantageous in econometric and statistical
contexts where rapid and accurate calculation of Kendall's correlation
coefficient is desirable. Benchmarks demonstrate substantial performance gains
over the base R implementation, especially for large datasets.