JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Goodness-of-Fit Tests for the Ordinal Response Models with Misspecified Links
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Goodness-of-Fit Tests for the Ordinal Response Models with Misspecified Links
Jeong, Kwang-Mo; Lee, Hyun-Yung;
  PDF(new window)
 Abstract
The Pearson chi-squared statistic or the deviance statistic is widely used in assessing the goodness-of-fit of the generalized linear models. But these statistics are not proper in the situation of continuous explanatory variables which results in the sparseness of cell frequencies. We propose a goodness-of-fit test statistic for the cumulative logit models with ordinal responses. We consider the grouping of a dataset based on the ordinal scores obtained by fitting the assumed model. We propose the Pearson chi-squared type test statistic, which is obtained from the cross-classified table formed by the subgroups of ordinal scores and the response categories. Because the limiting distribution of the chi-squared type statistic is intractable we suggest the parametric bootstrap testing procedure to approximate the distribution of the proposed test statistic.
 Keywords
Ordinal response;proportional odds model;goodness-of-fit;generalized link;ordinal scores;bootstrap;
 Language
English
 Cited by
1.
Notes on the Goodness-of-Fit Tests for the Ordinal Response Model,;;

응용통계연구, 2010. vol.23. 6, pp.1057-1065 crossref(new window)
1.
Notes on the Goodness-of-Fit Tests for the Ordinal Response Model, Korean Journal of Applied Statistics, 2010, 23, 6, 1057  crossref(new windwow)
 References
1.
Agresti, A. (2002). Categorical Data Analysis, 2nd ed., Wiley, New York

2.
Bull, S. (1994). Analysis of Attitudes toward Workplace Smoking Restrictions, In: N. Lange, L. Ryan, D. Billard, L. Conquest, and J. Greenhouse (eds.): Case Studies in Biometry. Wiley, New York, 249-271

3.
Chernoff, H. and Lehmann, E. L. (1954). The use of maximum likelihood estimation in Chi-square test of goodness of fit, The Annals of Mathematical Statistics, 25, 579-586 crossref(new window)

4.
Hosmer, D. W. and Lemeshow, S. (1980). Goodness-of-fit tests for the multiple logistic regression model, Communications in Statistics, Theory and Methods, 9, 1043-1069 crossref(new window)

5.
Kuss, O. (2002), Global goodness-of-fit tests in logistic regression with sparse data, Statistics in Medicine, 21, 3789-3801 crossref(new window)

6.
Lipsitz, S. R., Fitzmaurice, G. M. and Molenberghs, G. (1996). Goodness-of-fit tests for ordinal response regression models, Applied Statistics, 45, 175-190 crossref(new window)

7.
Moore, D. S. and Spruill, M. C. (1975). Unified large sample theory of general Chi-squared statistics for tests of fit, The Annals of Statistics, 3, 599-616

8.
Pigeon, J. G. and Heyse, J. F. (1999). An improved goodness-of-fit statistic for probability prediction models, Biometrical Journal, 41, 71-82 crossref(new window)

9.
Pulkstenis, E. and Robinson, T. J. (2004). Goodness-of-fit tests for ordinal response regression mod-els, Statistics in Medicine, 23, 999-1014 crossref(new window)

10.
Stukel, T. A. (1988). Generalized logistic models, Journal of the American Statistical Association, 3, 426-431