Recent work has shown that there may be disadvantages in the use of the chi-square-like goodness-of-fit tests for the logistic regression model proposed by Hosmer and Lemeshow that use fixed groups of the estimated probabilities. A particular concern with these grouping strategies based on estimated
The Performance of Goodness of Fit Tests for Logistic Regression with Discrete Covariates
β Scribed by L. R. Korn; D. W. Hosmer; S. Lemeshow
- Publisher
- John Wiley and Sons
- Year
- 1986
- Tongue
- English
- Weight
- 555 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0323-3847
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β¦ Synopsis
This paper considers the distribution of previously proposed goodnees of fit teets when nome or all of the covariates are dichotomous variables. The simulations show that of the statistics suggeated for teeting fit only one appears suitable for um with discrete covariates. This statistic urns conditional maximum likelihood estimates and groups the estimated probabilities into groups of equal size or into groups baaed on the patterns of the covariates when them are few in number.
π SIMILAR VOLUMES
## A batract A number of statistics have recently been proposed t o asssess the fit of the multiple logistic regression model in both prospective and retrospective studies involving two independent samples as well as in cross sectional studies. These statistics are not appropriate for assessing fi
The distribution of the Hosmer-Lemeshow chi-square type goodness-of-fit tests (Gg, I? for the logistic regremion model are examined via simulations designed to examine their behamor when most of the estimated probabilities are small or are expected to fall in a few deciles. The results of the simula