Nonparametric Tests for Complete Data

Vilijandas Bagdonavicius

ISBN 10: 1848212690 / ISBN 13: 9781848212695

Published by Wiley Jan 2011, 2011

About this Item

Neuware - This book concerns testing hypotheses in non-parametric models.Classical non-parametric tests (goodness-of-fit, homogeneity,randomness, independence) of complete data are considered. Most ofthe test results are proved and real applications are illustratedusing examples. Theories and exercises are provided. The incorrectuse of many tests applying most statistical software is highlightedand discussed.

Seller Inventory # 9781848212695

Bibliographic Details

• Title: Nonparametric Tests for Complete Data
• Publisher: Wiley Jan 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 1848212690
• ISBN 13: 9781848212695
• Binding: Buch
• Condition: Neu
• Dimensions: 234W x 157H x 23D millimeters 234 millimeters width by 157 millimeters height by 23 millimeters depth
• Weight: 617 grams

About this title

Synopsis

Statistical analysis of data sets usually involves construction of a statistical model of the distribution of data within the available sample – and by extension the distribution of all data of the same category in the world. Statistical models are either parametric or non-parametric – this distinction is based on whether or not the model can be described in terms of a finite-dimensional parameter – and the models must be tested to ascertain whether or not they conform to the data, or are accurate.

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

About the Author

Vilijandas Bagdonavicius is Professor of Mathematics at the University of Vilnius in Lithuania. His main research areas are statistics, reliability and survival analysis.

Julius Kruopis is Associate Professor of Mathematics at the University of Vilnius in Lithuania. His main research areas are statistics and quality control.

Mikhail S. Nikulin is a member of the Institute of Mathematics in Bordeaux, France.