Covariate Measurement Error In Logistic Regression
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publishersPoliciesContact Project Euclidfor Researchers Manage my accountAccessing Project EuclidAccess levelsPay-per-view and print-on-demandfor Librarians Manage my accountAccess levelsCollections, titles, and orderingLibrarian toolsfor Publishers Manage my accountYour publication in Project EuclidDiscovery service partnersPublisher tools The Annals of StatisticsInfoCurrent issueAll issuesSearch ← Previous articleTOCNext article → Ann. Statist. error in variables Volume 13, Number 4 (1985), 1335-1351.Covariate Measurement Error in Logistic RegressionLeonard A. Stefanski and Raymond J. Carroll More by Leonard A. StefanskiSearch this author in:Google ScholarProject Euclid More by Raymond J. CarrollSearch this author in:Google ScholarProject Euclid Full-text: Open access PDF File (1445 KB) AbstractArticle info and citationFirst pageAbstract In a logistic regression model when covariates are subject to measurement error the naive estimator, obtained by regressing on the observed covariates, is asymptotically biased. We introduce a bias-adjusted estimator and two estimators appropriate for normally distributed measurement errors -a functional maximum likelihood estimator and an estimator which exploits the consequences of sufficiency. The four proposals are studied asymptotically under conditions which are appropriate when the measurement error is small. A small Monte Carlo study illustrates the superiority of the measurement-error estimators in certain situations. Article informationSourceAnn. Statist. Volume 13, Number 4 (1985), 1335-1351.DatesFirst available in Project Euclid:
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or through your institution. If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader. The Annals of Statistics Vol. 13, No. 4, Dec., 1985 Covariate Measuremen... Covariate Measurement Error in Logistic Regression Leonard A. Stefanski and Raymond J. https://projecteuclid.org/euclid.aos/1176349741 Carroll The Annals of Statistics Vol. 13, No. 4 (Dec., 1985), pp. 1335-1351 Published by: Institute of Mathematical Statistics Stable URL: http://www.jstor.org/stable/2241358 Page Count: 17 Read Online (Free) Download ($19.00) Subscribe ($19.50) Cite this Item Cite This Item Copy Citation Export Citation Export to RefWorks Export a RIS file (For EndNote, ProCite, Reference Manager, Zotero…) Export a Text file (For BibTex) Note: Always review your references and make any necessary corrections before using. https://www.jstor.org/stable/2241358 Pay attention to names, capitalization, and dates. × Close Overlay Journal Info The Annals of Statistics Description: The Annals of Statistics publishes research papers of the highest quality reflecting the many facets of contemporary statistics. Primary emphasis is placed on importance and originality, not on formalism. The discipline of statistics has deep roots in both mathematics and in substantive scientific fields. Mathematics provides the language in which models and the properties of statistical methods are formulated. It is essential for rigor, coherence, clarity and understanding. Consequently, our policy is to continue to play a special role in presenting research at the forefront of mathematical statistics, especially theoretical advances that are likely to have a significant impact on statistical methodology or understanding. Substantive fields are essential for continued vitality of statistics since they provide the motivation and direction for most of the future developments in statistics. We thus intend to also publish papers relating to the role of statistics in interdisciplinary investigations in all fields of natural, technical and social science. A third force that is reshaping statistics is the computational revolution, and The Annals will also welcome developments in this area. Coverage: 1973-2012 (Vol. 1, No. 1 - Vol. 40, No. 6) Moving Wall Moving Wall: 3 years (What is the moving wall?) Moving Wa
nonparametric maximum likelihood estimation Rabe-Hesketh, Sophia, Pickles, Andrew and Skrondal, Anders (2003) Correcting for covariate measurement logistic regression error in logistic regression using nonparametric maximum likelihood estimation. Statistical Modelling, 3 (3). pp. 215-232. ISSN 1471-082X Full text not available from this repository. Published logistic regression controlling item via DOI Item Type: Article Official URL: http://smj.sagepub.com/ Additional Information: © Sage Publications 2003 Library of Congress subject classification: H Social Sciences > HA Statistics Sets: Departments > MethodologyDepartments > Statistics Date Deposited: 10 Oct 2008 14:09 URL: http://eprints.lse.ac.uk/17102/ Actions (login required) Record administration - authorised staff only Mission Statement & FAQs | Contact us | Takedown Policy | LSE Experts | LSE Research Online supports OAI 2.0 with a base URL of http://eprints.lse.ac.uk/cgi/oai2