Go to the content. | Move to the navigation | Go to the site search | Go to the menu | Contacts | Accessibility

| Create Account

Guolo, Annamaria (2007) Robust Techniques for Measurement Error Correction in Case-Control Studies: A Review. [Working Paper] WORKING PAPER SERIES, 2/2007 . , PADOVA

Full text disponibile come:

PDF Document

Abstract (english)

Measurement error affecting the independent variables in regression models is a common problem in many scientific areas. It is well known that the implications of ignoring measurement errors in inferential procedures may be substantial, often turning out in unreliable results. Many different measurement error correction techniques have been suggested in literature since the 80's. Most of them require many assumptions on the involved variables to be satisfied. However, it may be usually very hard to check whether these assumptions are satisfied, mainly because of the lack of information about the unobservable and mismeasured phenomenon. Thus, alternatives based on weaker assumptions on the variables may be preferable, in that they offer a gain in robustness of results. In this paper, we provide a review of robust techniques to correct for measurement errors affecting the covariates.
Attention is paid to methods which share properties of robustness against misspecifications of relationships between variables. Techniques are grouped according to the kind of underlying modeling assumptions and inferential methods.
Details about the techniques are given and their applicability is discussed. The basic framework is the epidemiological setting, where literature about the measurement error phenomenon is very substantial. The focus will be mainly on case-control studies.

Statistiche Download - Aggiungi a RefWorks
EPrint type:Working Paper
Anno di Pubblicazione:January 2007
More information:Pubblicato anche in: Statistical Methods in Medical Research - Volume 17, pp. 555-580
Key Words:case-control study, empirical likelihood, estimating equation, kernel regression, logistic regression, measurement error, normal mixture, quasi-likelihood
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:8805
Depositato il:25 May 2015 10:26
Simple Metadata
Full Metadata
EndNote Format

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record