On goodness-of-fit of logistic regression model

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dc.contributor.author Liu, Ying
dc.date.accessioned 2007-12-19T20:15:57Z
dc.date.available 2007-12-19T20:15:57Z
dc.date.issued 2007-12-19T20:15:57Z
dc.identifier.uri http://hdl.handle.net/2097/530
dc.description.abstract Logistic regression model is a branch of the generalized linear models and is widely used in many areas of scientific research. The logit link function and the binary dependent variable of interest make the logistic regression model distinct from linear regression model. The conclusion drawn from a fitted logistic regression model could be incorrect or misleading when the covariates can not explain and /or predict the response variable accurately based on the fitted model- that is, lack-of-fit is present in the fitted logistic regression model. The current goodness-of-fit tests can be roughly categorized into four types. (1) The tests are based on covariate patterns, e.g., Pearson's Chi-square test, Deviance D test, and Osius and Rojek's normal approximation test. (2) Hosmer-Lemeshow's C and Hosmer-Lemeshow's H tests are based on the estimated probabilities. (3) Score tests are based on the comparison of two models, where the assumed logistic regression model is embedded into a more general parametric family of models, e.g., Stukel's Score test and Tsiatis's test. (4) Smoothed residual tests include le Cessie and van Howelingen's test and Hosmer and Lemeshow's test. All of them have advantages and disadvantages. In this dissertation, we proposed a partition logistic regression model which can be viewed as a generalized logistic regression model, since it includes the logistic regression model as a special case. This partition model is used to construct goodness-of- fit test for a logistic regression model which can also identify the nature of lack-of-fit is due to the tail or middle part of the probabilities of success. Several simulation results showed that the proposed test performs as well as or better than many of the known tests. en
dc.language.iso en_US en
dc.publisher Kansas State University en
dc.subject Logistic Regression en
dc.subject Goodness-of-Fit en
dc.title On goodness-of-fit of logistic regression model en
dc.type Dissertation en
dc.description.degree Doctor of Philosophy en
dc.description.level Doctoral en
dc.description.department Department of Statistics en
dc.description.advisor Shie-Shien Yang en
dc.subject.umi Statistics (0463) en
dc.date.published 2007 en
dc.date.graduationmonth December en

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