Journals / Cumhuriyet Science Journal / 2021 / Cilt: 42 - Sayı: 2

A study on Freeman-Tukey test statistic under the symmetry model for square contingency tables

Pages
413–421
Publication Date
2022-10-12
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results of simulation study and artificial data study show that Freeman- Tukey’s 𝑇2 test statistic does not converge to chi-squared distribution for both sparse and nonsparse square contingency tables.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The symmetry model is the basic model in the analysis of square contingency tables.
📊
Key Findings & Data:Multiple test statistics have been developed for the goodness of fit test.
💡
Conclusion & Impact:The results of simulation study and artificial data study show that Freeman- Tukey’s 𝑇2 test statistic does not converge to chi-squared distribution for both sparse and nonsparse square contingency tables.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The symmetry model is the basic model in the analysis of square contingency tables. Multiple test statistics have been developed for the goodness of fit test. Freeman-Tukey test statistics is appropriate to be used in large samples. However, the required sample size to use the Freeman- Tukey test statistics is not clear. In this paper, the asymptotic properties of Freeman-Tukey test statistic are discussed via extensive Monte-Carlo simulation study. The Freeman-Tukey test statistic is compared with members of power-divergence family test statistic under the symmetry model. The results of simulation study are evaluated based on the Type-I error and power of a test. The results of simulation study and artificial data study show that Freeman- Tukey’s 𝑇2 test statistic does not converge to chi-squared distribution for both sparse and nonsparse square contingency tables.

🔖

Cite This Article

ALTUN, G. (2022). A study on Freeman-Tukey test statistic under the symmetry model for square contingency tables. Cumhuriyet Science Journal, Cilt: 42 - Sayı: 2, 413–421. https://ajindex.com/articles/477633

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (16)

  1. Altun G., Karesel Olumsallık Tablolarında Model Uyumunun Sapma Ölçüsü ile Belirlenmesi, PD Thesis, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, (2018)
  2. Goodman L. A., Multiplicative models for square contingency tables with ordered categories, Biometrika., 66(3) (1979) 413-418
  3. Fienberg S. E., The use of chi-squared statistics for categorical data problems, Journal of the Royal Statistical Society. Series B (Methodological.), 41(1) (1979) 54-64
  4. Horn S. D., Goodness-of-fit tests for discrete data: a review and an application to a health impairment scale, Biometrics., 33(1) (1977) 237-247
  5. Watson G. S., Some recent results in chi-square goodness-of-fit tests, Biometrics., 15(3) (1959) 440-468
  6. Cochran W. G., The χ2 test of goodness of fit, The Annals of Mathematical Statistics., 23(3) (1952) 315-345
  7. Tate M. W., Hyer L. A., Inaccuracy of the X2 test of goodness of fit when expected frequencies are small, Journal of the American Statistical Association., 68(344) (1973) 836-841
  8. Yarnold J. K., The minimum expectation in X2 goodness of fit tests and the accuracy of approximations for the null distribution, Journal of the American Statistical Association., 65 (330) (1970) 864-886
  9. Fisher R. A., Statistical methods for research workers,13th ed. New York: Hafner Publishing Co, (1958) 356
  10. Roscoe J. T., Byars J. A., An investigation of the restraints with respect to sample size commonly imposed on the use of the chi-square statistic, Journal of the American Statistical Association., 66(336) (1971) 755-759
  11. Agresti A, Categorical data analysis, 2nd ed. New Jersey: John Wiley & Sons, (2003) 482
  12. Koehler K. J., Goodness-of-fit tests for log-linear models in sparse contingency tables, Journal of the American Statistical Association., 81 (394) (1986) 483-493
  13. Koehler, K. J., Larntz, K., An empirical investigation of goodness-of-fit statistics for sparse multinomials. Journal of the American Statistical Association., 75(370) (1980) 336-344
  14. Larntz K., Small-sample comparisons of exact levels for chi-squared goodness-of-fit statistics, Journal of the American Statistical Association., 73(362) (1978) 253-263
  15. Haberman S. J., A warning on the use of chisquared statistics with frequency tables with small expected cell counts, Journal of the American Statistical Association., 83(402) (1988) 555-560
  16. Cressie N., Read T. R., Pearson's X2 and the loglikelihood ratio statistic G2: a comparative review, In