Non-parametric Tests for Complete Data

Mikhail Nikulin (1) , Bagdonavicius Vilijandas (2) , Kruopis Julius (2)

1 IMB - Institut de Mathématiques de Bordeaux
2 Departement of Statistics, Vilnius University

The book addressed the testing of hypotheses in non-parametric models in the general case for complete data samples. Classical non-parametric tests (goodness-of-fit , homogenneity, randomness, independence) of complete data are considered and explained. Tests features include the chi-squared and modified chi-squared tests, rank and homogeneity tests, and most of the test results are proved, with real applicatiuons illustrated using examples. The incorrect use of many tests, and their applications using commonly deployed statistical software is highlighted and discuss.

Mots clés: Complete data; chi-squared test; goodness-of-fit test; empirical processes; rank test; non-parametric test; maximum likelihood estimator

Domaines: Statistiques [math.ST]; Théorie [stat.TH]; Applications [stat.AP]

Mikhail Nikulin, Bagdonavicius Vilijandas, Kruopis Julius (Dir.). Non-parametric Tests for Complete Data. ISTE-WILEY, 308 p., 2011, 9 781 848 212 695. ⟨hal-00782912⟩