Improving tests for discrete small sample data Nobuhiro Taneichi, Yuri Sekiya

Format:

Book

Author/Creator:

Taneichi, Nobuhiro, author.

Sekiya, Yuri, author.

Series:

SpringerBriefs in statistics. JSS research series in statistics 2364-0065

JSS research series in statistics 2364-0065

Language:

English

Subjects (All):

Mathematical statistics.

Sampling (Statistics).

Physical Description:

1 online resource

illustration

Place of Publication:

Singapore Springer 2026

Summary:

This book provides a guide for improving test statistics that are based on phi-divergence for discrete models, which include various kinds of independence models of contingency tables as well as generalized linear models of binary data. The improvements are based on the theory of asymptotic expansion and lead to correct conclusions of a test even when sample sizes are not large. Without such an improvement, there is a risk that the results of a test will lead to the opposite conclusion, as a limiting distribution is used for an approximated distribution of test statistics. Mainly, for the phi-divergence family of statistics that include Pearson's chi-square statistic, the log-likelihood ratio statistic, and the power divergence family of statistics as a special case, the book derives the improvement of statistics as transformed statistics. This accomplishment is achieved by using the expression of approximation of the distribution of original phi-divergence statistics based on Edgeworth expansion. For an independence model of a contingency table, a complete independence model, an independence model among a group of factors, and a conditional independence model are considered. The test statistics of a contingency table for a log-linear model are also presented for consideration. Additionally, the selection of statistics for which the distribution is close to the limiting distribution is discussed using the evaluation of second-order correction of moments

Contents:

Chapter 1.Purpose of this book

Chapter 2.Preliminary results

Chapter 3. Transformed statistic for the test of independence in J x K contingency table

Chapter 4. A transformed statistic for the test of complete independence in a contingency table

Chapter 5. A transformed statistic for the test of conditional independence in a J x K x L table

Chapter 6. A transformed goodness-of-fit statistic for a generalized linear model of binary data

Chapter 7. A transformed goodness-of-fit statistic for a loglinear model in a contingency table

Chapter 8. The selection of statistics based on a second-order correction term when data are not so large

Chapter 9. Constructing a new statistic NT for testing complete independence in contingency tables

Chapter 10. Summary and Conclusion

Notes:

Includes bibliographical references

Online resource; title from PDF title page (SpringerLink, viewed June 11, 2026)

ISBN:

9789819553013

9819553016

OCLC:

1594769412

Access Restriction:

Restricted for use by site license