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Communications in Statistics - Simulation and Computation Volume 23, 1994 - Issue 1
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Original Articles

Two-step jackknife bias reduction for logistic regression mles

S.B Bull Samuel Lunenfeld Research Institute of Mount Sinai Hospital and Department of Preventive Medicine and Biostatistics, University of Toronto, Suite 850, 600 University Ave, Toronto, Ontario, M5G 1X5, Canada
,
W.W Hauck Department of Epidemiology and Biostatistics, University of California, P.O. Box 0560, San Francisco, 94143, USA
&
C.M.T Greenwood Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Suite 850, 600 University Ave, M5G 1X5, Toronto, Ontario, Canada
Pages 59-88 | Received 01 Dec 1992, Published online: 27 Jun 2007

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Nowrin Nusrat & M. S. Rahman. (2022) Dealing with separation or near-to-separation in the model for multinomial response with application to childhood health seeking behavior data from a complex survey. Journal of Applied Statistics 49:16, pages 4254-4277.
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Norman R. Draper. (1998) Applied regression analysis bibliography update 1994-97. Communications in Statistics - Theory and Methods 27:10, pages 2581-2623.
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Articles from other publishers (4)

Pragya SurEmmanuel J. Candès. (2019) A modern maximum-likelihood theory for high-dimensional logistic regression. Proceedings of the National Academy of Sciences 116:29, pages 14516-14525.
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M. Revan Özkale & Engin Arıcan. (2018) A first-order approximated jackknifed ridge estimator in binary logistic regression. Computational Statistics 34:2, pages 683-712.
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Emma M. Iglesias & Garry D. A. Phillips. (2017) The use of bias correction versus the Jackknife when testing the mean reversion and long term mean parameters in continuous time models. Monte Carlo Methods and Applications 23:3, pages 159-164.
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Shelley B. Bull, Carmen Mak & Celia M.T. Greenwood. (2002) A modified score function estimator for multinomial logistic regression in small samples. Computational Statistics & Data Analysis 39:1, pages 57-74.
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