Statistical Regression with Measurement Error

Covering the field of statistics called measurement error models, this work includes a full discussion of functional and structural models as well as the more general ultrastructural model. The material is presented at a level appropriate for beginning graduate students and includes problems at the end of each chapter to aid learning. Computational methods are considered, and the rationale is to provide an intermediate level survey of the field of measurement error models without too much mathematical detail. Topics covered include: model identifiability; parameter estimation; confidence intervals; asymptotic theory; finite sample properties; orthogonal regression; modified lease squares methods; instrumental variable methods; the linear and non-linear Berkson Model; calibration; and computational methods.