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Some recent advances in measurement error models and methods

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The paper gives an account of these endeavors. In another context, when data are of a categorical nature, classification errors play a similar role as measurement errors in continuous data. The paper also reviews some recent advances in this field.
Year of publication:
2005
Authors: Schneeweiss, Hans ; Augustin, Thomas
Publisher: München : Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Subject: Regression | Schätztheorie | Theorie | Measurement errors | error in variables | misclassification | efficiency comparison | survival analysis
Series:
Type of publication: Book / Working Paper
Type of publication (narrower categories): Working Paper
Language: English
Other identifiers:
10.5282/ubm/epub.1821 [DOI]
501294406 [GVK]
hdl:10419/31008 [Handle]
Classification: C13 - Estimation ; C20 - Econometric Methods: Single Equation Models. General ; C24 - Truncated and Censored Models ; C25 - Discrete Regression and Qualitative Choice Models
Source:
Persistent link: https://ebvufind01.dmz1.zbw.eu/10010332971
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