Logistic Regression With Measurement Error
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. 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 functi
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign https://projecteuclid.org/euclid.aos/1176349741 up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top How can I correct for measurement error in the dependent variable in a logit regression? up vote 7 down vote favorite 1 I'm running a binary logit regression where I know the dependent variable is miscoded in a small http://stats.stackexchange.com/questions/9431/how-can-i-correct-for-measurement-error-in-the-dependent-variable-in-a-logit-reg percentage of cases. So I'm trying to estimate $\beta$ in this model: $prob(y_i) = 1/(1 + e^{-z_i})$ $z_i = \alpha + X_i\beta$ But instead of the vector $Y$, I have $\tilde{Y}$, which includes some random errors (i.e. $y_i = 1$, but $\tilde{y_i} = 0$, or vice versa, for some $i$). Is there a (reasonably) simple correction for this problem? I know that logit has some nice properties in case-control studies. It seems likely that something similar applies here, but I haven't been able to find a good solution. A few other constraints: this is a text-mining application, so the dimensions of $X$ are large (in the thousands or tens of thousands). This may rule out some computationally intensive procedures. Also, I don't care about correctly estimating $\alpha$, only $\beta$. logistic measurement-error share|improve this question edited Apr 11 '11 at 14:38 GaBorgulya 2,662818 asked Apr 11 '11 at 14:03 Abe 6802918 add a comment| 2 Answers 2 active oldest votes up vote 2 down vote This situation is often referred to as misclassification error. This paper my help you correctly estimating $\beta$. EDIT: I found relevant-looking papers using http:/
Login Help Contact Us About Access You are not currently logged in. Access your https://www.jstor.org/stable/2241358 personal account or get JSTOR access through your library or other institution: login Log in to your personal account 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 logistic regression 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. Carroll The Annals of Statistics Vol. 13, No. 4 (Dec., 1985), pp. 1335-1351 Published by: Institute logistic regression with 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. 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
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