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999			_c4449 _d4449
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008			230302b ||||| |||| 00| 0 eng d
020			_a9780367194840
082			_a519.5 _bKOL
100			_aKolassa, John E. _910427
245			_aAn introduction to nonparametric statistics
260			_bCRC Press _aBoco Raton _c2021
300			_axii, 212 p.
365			_aGBP _b79.99
490			_aText in statistical science
504			_aTable of Contents Background One-Sample Nonparametric Inference Two-Sample Testing Methods for Three or More Groups Group Differences with Blocking Bivariate Methods Multivariate Analysis Density Estimation Regression Function Estimates Resampling Techniques Appendices
520			_aAn Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data.　Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well.　These techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression. Attention is paid to the intellectual development of the field, with a thorough review of bibliographical references.　Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign, Kruskal-Wallis, Friedman, Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics.　The course is best taken after an introductory course in statistical methodology, elementary probability, and regression.　Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.
650			_aNonparametric statistics _97513
942			_2ddc _cBK