Mehr lesen
Excerpt from The Computation and Use of the Asymptotic Covariance Matrix for Measurement Error Models
This problem goes under various names, including errors in variables, generalized least squares, orthogonal distance regression, and measurement error models. We prefer measurement error models in deference to the book of Fuller [1987] that presents the definitive modern treatment of the problem. We also use the term orthogonal distance regression since, as we show in §2, it is a useful geometric description of the problem actually solved.
As in the ordinary least squares case, when using measurement error models one is frequently interested in constructing. Confidence regions and or confidence intervals for the model parameters. To this end, Fuller derives the asymptotic form of the covariance matrix-and uses it in several examples. It is well known, however, that for nonlinear models in general, and for measurement error models in particular, confidence regions and intervals constructed using the covariance matrix are only approximate.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
