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The Hosmer–Lemeshow test is a statistical test for goodness of fit and calibration for logistic regression models. It is used frequently in risk prediction models. The test assesses whether or not the observed event rates match expected event rates in subgroups of the model population.
Kolmogorov–Smirnov test; Cramér–von Mises criterion; Anderson–Darling test; Berk-Jones tests [1] [2] Shapiro–Wilk test; Chi-squared test; Akaike information criterion; Hosmer–Lemeshow test; Kuiper's test; Kernelized Stein discrepancy [3] [4] Zhang's Z K, Z C and Z A tests [5] Moran test; Density Based Empirical Likelihood Ratio tests [6]
The interpretation of the β j parameter estimates is as the additive effect on the log of the odds for a unit change in the j the explanatory variable. In the case of a dichotomous explanatory variable, for instance, gender e β {\displaystyle e^{\beta }} is the estimate of the odds of having the outcome for, say, males compared with females.
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Hosmer–Lemeshow test, a quality of fit statistic that can be used for binary data; Pearson's chi-squared test, an alternative quality of fit statistic for generalized linear models for count data; Peirce's criterion, a rule for eliminating outliers from data sets
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It should be: -2ln [ likelihood of fitted model / likelihood of saturated model ] Which is the correct definition from Hosmer and Lemeshow's Applied logistic regression p. 13. — Preceding unsigned comment added by 62.242.0.66 ( talk ) 11:29, 25 January 2013 (UTC) [ reply ]