John P. Buonaccorsi

Measurement Error

Models, Methods, and Applications

• Gebundenes Buch

Produktbeschreibung

Measurement Error provides an understanding of measurement error, the effects of ignoring it, and how to correct for these effects. The book focuses on the models and methods involved and demonstrates how they can be implemented in practice. Keeping theory to a minimum with an appendix of theoretical background, it presents numerous examples from biostatistics and epidemiology as well as ecology and the social sciences. The author implements these examples using available Stata routines and his own SAS programs. Topics covered include misclassification in estimation, measurement error in inference, predictors, and time series.

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Produktdetails

• Verlag: Chapman and Hall/CRC
• Seitenzahl: 464
• Erscheinungstermin: 2. März 2010
• Englisch
• Abmessung: 240mm x 161mm x 29mm
• Gewicht: 857g
• ISBN-13: 9781420066562
• ISBN-10: 1420066560
• Artikelnr.: 23096965

Autorenporträt

John P. Buonaccorsi is a professor in the Department of Mathematics and Statistics at the University of Massachusetts, Amherst.

Inhaltsangabe

Introduction. Misclassification in Estimating a Proportion.
Misclassification in Two-Way Tables. Simple Linear Regression. Multiple
Linear Regression. Measurement Error in Regression: A General Overview.
Binary Regression. Linear Models with Nonadditive Error. Nonlinear
Regression. Error in the Response. Mixed/Longitudinal Models. Time Series.
Background Material. References. Indices.