Advanced search
Publication Cover
Technometrics Volume 22, 1980 - Issue 4
Submit an article Journal homepage
34
Views
18
CrossRef citations to date
0
Altmetric
Original Articles

A Note on Bias Correction in Maximum likelihood Estimation With logistic Discrimination

G. J. McLachlan Department of Mathematics, University of Queensland, St. Lucia, 4067, Brisbane, Queensland, Australia
Pages 621-627 | Published online: 23 Mar 2012

Citations (18)

Keep up to date with the latest research on this topic with citation updates for this article.

Subscribe to citation updates

Read on this site (10)

AndreaA. Prudente & GaussM. Cordeiro. (2010) Generalized Weibull Linear Models. Communications in Statistics - Theory and Methods 39:20, pages 3739-3755.
Read now
GaussM. Cordeiro & MargarethC. Toyama Udo. (2008) Bias Correction in Generalized Nonlinear Models with Dispersion Covariates. Communications in Statistics - Theory and Methods 37:14, pages 2219-2225.
Read now
L. C. C. Montenegro, E. A. Colosimo, G. M. Cordeiro & F. R.B. Cruz . (2004) Bias correction in the cox regression model . Journal of Statistical Computation and Simulation 74:5, pages 379-386.
Read now
F. R. B. Cruz, E. A. Colosimo & J. MacGregor Smith. (2004) Sample Size Corrections for the Maximum Partial Likelihood Estimator. Communications in Statistics - Simulation and Computation 33:1, pages 35-47.
Read now
E.A. Colosimo, A.F. Silva & F.R.B. Cruz. (2000) Bias evaluation in the proportional hazards model. Journal of Statistical Computation and Simulation 65:1-4, pages 191-201.
Read now
Denise A. Botter & Cordeiro M. Denise. (1998) Improved estimators for generalized linear models with dispersion covariates. Journal of Statistical Computation and Simulation 62:1-2, pages 91-104.
Read now
Gauss M. Cordeiro, Klaus L. P. Vasconcellos & Maria Luiza F. Santos. (1998) on the second-order bias of parameter estimates in nonlinear regression models with student t errors. Journal of Statistical Computation and Simulation 60:4, pages 363-378.
Read now
Gauss M. Cordeiro, Enivaldo C. Da Rocha, Jacira Guiro C. Da Rocha & Francisco Cribari-Neto. (1997) Bias-corrected maximum likelihood estimation for the beta distribution. Journal of Statistical Computation and Simulation 58:1, pages 21-35.
Read now
S.B Bull, W.W Hauck & C.M.T Greenwood. (1994) Two-step jackknife bias reduction for logistic regression mles. Communications in Statistics - Simulation and Computation 23:1, pages 59-88.
Read now
Tzu-Cheg Kao & GeorgeP. McCabe. (1991) Optimal Sample Allocation for Normal Discrimination and Logistic Regression under Stratified Sampling. Journal of the American Statistical Association 86:414, pages 432-436.
Read now

Articles from other publishers (8)

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.
Crossref
Robert H. Lyles, Ying Guo & Sander Greenland. (2012) Reducing bias and mean squared error associated with regression-based odds ratio estimators. Journal of Statistical Planning and Inference 142:12, pages 3235-3241.
Crossref
Gauss M. Cordeiro & Peter McCullagh. (1991) Bias Correction in Generalized Linear Models. Journal of the Royal Statistical Society Series B: Statistical Methodology 53:3, pages 629-643.
Crossref
G.J. McLachlan. (1986) Assessing the performance of an allocation rule. Computers & Mathematics with Applications 12:2, pages 261-272.
Crossref
S.C. Choi. (1986) Discrimination and classification: Overview. Computers & Mathematics with Applications 12:2, pages 173-177.
Crossref
G.J. MCLACHLAN. 1986. Statistical Methods of Discrimination and Classification. Statistical Methods of Discrimination and Classification 261 272 .
S.C. CHOI. 1986. Statistical Methods of Discrimination and Classification. Statistical Methods of Discrimination and Classification 173 177 .
Andreas Hackethal, Sven Thorsten Jakusch & Steffen Meyer. (2015) Taring All Investors with the Same Brush? Evidence for Heterogeneity in Individual Preferences from a Maximum Likelihood Approach. SSRN Electronic Journal.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.