{"title":"非参数测试:分布未知时的测试。","authors":"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts","doi":"10.1079/9781845932756.0287","DOIUrl":null,"url":null,"abstract":"Abstract\n This chapter introduces several other commonly used non-parametric tests: the sign test, the Wilcoxon signed rank test, the Wilcoxon rank sum test, the Kruskal-Wallis test, the runs test, and Spearman's rank correlation test. Non-parametric tests do not require knowledge or estimates of the parameter values. They can be performed without uniquely identifying the distribution, or its parameters. The use of these non-parametric tests in forestry applications are given in this chapter.","PeriodicalId":413890,"journal":{"name":"Introductory probability and statistics: applications for forestry and natural sciences","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-parametric tests: testing when distributions are unknown.\",\"authors\":\"A. Kozak, R. Kozak, C. Staudhammer, S. B. Watts\",\"doi\":\"10.1079/9781845932756.0287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract\\n This chapter introduces several other commonly used non-parametric tests: the sign test, the Wilcoxon signed rank test, the Wilcoxon rank sum test, the Kruskal-Wallis test, the runs test, and Spearman's rank correlation test. Non-parametric tests do not require knowledge or estimates of the parameter values. They can be performed without uniquely identifying the distribution, or its parameters. The use of these non-parametric tests in forestry applications are given in this chapter.\",\"PeriodicalId\":413890,\"journal\":{\"name\":\"Introductory probability and statistics: applications for forestry and natural sciences\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Introductory probability and statistics: applications for forestry and natural sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1079/9781845932756.0287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Introductory probability and statistics: applications for forestry and natural sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1079/9781845932756.0287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-parametric tests: testing when distributions are unknown.
Abstract
This chapter introduces several other commonly used non-parametric tests: the sign test, the Wilcoxon signed rank test, the Wilcoxon rank sum test, the Kruskal-Wallis test, the runs test, and Spearman's rank correlation test. Non-parametric tests do not require knowledge or estimates of the parameter values. They can be performed without uniquely identifying the distribution, or its parameters. The use of these non-parametric tests in forestry applications are given in this chapter.
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