Rank tests form a class of statistical procedures that combine great simplicity with surprising power. Since their development in the 1940s and 1950s, they have taken their place as strong competitors of the more classical normal theory methods. Rank tests apply only to relatively simple solutions, such as one-, two-, and s-sample problems, and testing for independence and randomness; for these situations they are often the method of choice. This reprint of a classic reference book offers a thorough description of these tests and the estimating procedures derived from them, and gives an account of their properties. Although the field of rank tests has seen little change, important new methodologies have sprung up that also serve the purpose of freeing statistics from the unrealistic model assumptions that so frequently invalidate its applications. All the tests discussed here are now available in a variety of statistical software packages.
Rank tests form a class of statistical procedures that combine great simplicity with surprising power. Since their development in the 1940s and 1950s, they have taken their place as strong competitors of the more classical normal theory methods. Rank tests apply only to relatively simple solutions, such as one-, two-, and s-sample problems, and testing for independence and randomness; for these situations they are often the method of choice. This reprint of a classic reference book offers a thorough description of these tests and the estimating procedures derived from them, and gives an account of their properties. Although the field of rank tests has seen little change, important new methodologies have sprung up that also serve the purpose of freeing statistics from the unrealistic model assumptions that so frequently invalidate its applications. All the tests discussed here are now available in a variety of statistical software packages.
Über den Autor
Erich L. Lehmann is Professor Emeritus of Statistics at the University of California at Berkeley. He is a member of the American and National Academies, a former Editor of the Annals of Mathematical Statistics, and President of the Institute of Mathematical Statistics. He holds honorary degrees from the Universities of Chicago and Leiden, and was awarded the Wilks and Noether prizes. He is also the author of Testing Statistical Hypotheses, Theory of Point Estimation, and Elements of Large-Sample Theory, all published by Springer. Two more elementary books, Basic Concepts of Probability and Statistics (joint with Hodges) and Nonparametrics have recently been reissued by SIAM and Springer, respectively.
Zusammenfassung
Rank tests form a class of statistical procedures that combine great simplicity with surprising power. Since their development in the 1940s and 1950s, they have taken their place as strong competitors of the more classical normal theory methods. Rank tests apply only to relatively simple solutions, such as one-, two-, and s-sample problems, and testing for independence and randomness; for these situations they are often the method of choice. This reprint of a classic reference book offers a thorough description of these tests and the estimating procedures derived from them, and gives an account of their properties. Although the field of rank tests has seen little change, important new methodologies have sprung up that also serve the purpose of freeing statistics from the unrealistic model assumptions that so frequently invalidate its applications. All the tests discussed here are now available in a variety of statistical software packages.
Inhaltsverzeichnis
Rank Tests for Comparing Two Treatments.- Comparing Two Treatments or Attributes in a Population Model.- Blocked Comparisons for Two Treatments.- Paired Comparisons in a Population Model and the One-Sample Problem.- The Comparison of More Than Two Treatments.- Randomized Complete Blocks.- Tests of Randomness and Independence.