Judging Inference Adequacy in Logistic Regression. Jennings, D. E. JASA, 81(394):471-476, Taylor & Francis, 1986.
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Abstract Inference for logistic regression based on the information matrix may be poor. This is noted in two examples in which confidence regions are examined. A measure to detect such inadequacies is presented; it judges the quadratic approximation to the likelihood surface, which justifies the usual procedure.
@article{jen86jud,
  title = {Judging {{Inference Adequacy}} in {{Logistic Regression}}},
  volume = {81},
  abstract = {Abstract Inference for logistic regression based on the information matrix may be poor. This is noted in two examples in which confidence regions are examined. A measure to detect such inadequacies is presented; it judges the quadratic approximation to the likelihood surface, which justifies the usual procedure.},
  number = {394},
  journal = {JASA},
  doi = {10.1080/01621459.1986.10478292},
  author = {Jennings, Dennis E.},
  year = {1986},
  keywords = {maximum-likelihood,confidence-intervals,logistic-model,binary-logistic-model},
  pages = {471-476},
  eprint = {http://amstat.tandfonline.com/doi/pdf/10.1080/01621459.1986.10478292},
  publisher = {Taylor & Francis},
  citeulike-article-id = {14516413},
  citeulike-attachment-1 = {jennings₈6<sub>j</sub>udging₁126896.pdf; /pdf/user/harrelfe/article/14516413/1126896/jennings₈6<sub>j</sub>udging₁126896.pdf; 1dbeca613b3a33b55c2ac67d014b1916d86b4f4b},
  citeulike-linkout-0 = {http://dx.doi.org/10.1080/01621459.1986.10478292},
  citeulike-linkout-1 = {http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1986.10478292},
  posted-at = {2018-01-14 00:06:47},
  priority = {0}
}

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