Nonparametric Tests for Complete Data (ISTE)

Book Preface

Testing hypotheses in non-parametric models are discussed in this book. A statistical model is non-parametric if it cannot be written in terms of a finite-dimensional parameter. The main hypotheses tested in such models are hypotheses on the probability distribution of elements of the following: data homogeneity, randomness and independence hypotheses. Tests for such hypotheses from complete samples are considered in many books on non-parametric statistics, including recent monographs by Maritz [MAR 95], Hollander andWolfe [HOL 99], Sprent and Smeeton [SPR 01], Govindarajulu [GOV 07], Gibbons and Chakraborti [GIB 09] and Corder and Foreman [COR 09].

This book contains tests from complete samples. Tests for censored samples can be found in our book Tests for Censored Samples [BAG 1 ].