Some recent advances in measurement error models and methods

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Paper ; No. 452

Classification and Topics

• Classification: Estimation: General ; Single Equation Models; Single Variables: General ; Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models ; Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
• Subject: Measurement errors ; error in variables ; misclassification ; efficiency comparison ; survival analysis ; Regression ; Schätztheorie ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Schneeweiss, Hans ; Augustin, Thomas
• Event: Veröffentlichung
• (who): Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
• (where): München
• (when): 2005

Further information

• DOI: doi:10.5282/ubm/epub.1821
• Handle: http://hdl.handle.net/10419/31008
• URN: urn:nbn:de:bvb:19-epub-1821-5
• Last update: 10.03.2025, 11:44 AM CET