A computer program has been written which performs a stepwise selection of variables for logistic regression using maximum likelihood estimation. The selection procedure is based on likelihood ratio tests for the coefficients. These tests are used in a forward selection and a backward elimination at
A Preliminary Investigation of Maximum Likelihood Logistic Regression versus Exact Logistic Regression
β Scribed by King, Elizabeth N; Ryan, Thomas P
- Book ID
- 121338005
- Publisher
- American Statistical Association
- Year
- 2002
- Tongue
- English
- Weight
- 244 KB
- Volume
- 56
- Category
- Article
- ISSN
- 0003-1305
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
We propose a maximum likelihood estimator (MLE) of the kappa coefficient from a 2 x 2 table when the binary ratings depend on patient and/or clinician effects. We achieve this by expressing the logit of the probability of positive rating as a linear function of the subject-specific and the rater-spe
General approaches to the fitting of binary response models to data collected in two-stage and other stratified sampling designs include weighted likelihood, pseudo-likelihood and full maximum likelihood. In previous work the authors developed the large sample theory and methodology for fitting of l
In this note we discuss the breakdown behavior of the maximum likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to inΓΏnity, but rather breaks down to zero when adding severe outliers to a data set. An example conΓΏrms this behavior.