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And the covariance matrix is specified as: And the [[Statistics/CovarianceMatrices|covariance matrix]] is specified as:

Measurement Error Models

Measurement Error Models (ISBN: 9780470316665) was written by Wayne A. Fuller in 1987.

A measurement Xt is decomposed into the true value xt and the measurement error ut.

When a linear model is constructed as Yt = β0 + β1xt + et but estimated as Yt = βˆ0 + βˆ1Xt = βˆ0 + βˆ1(xt + ut), then the expected values are characterized by:

exp1.svg

exp2.svg

And the covariance matrix is specified as:

covar.svg

We assume that xt, et, and ut are all independent.

The regression coefficient that has been attenuated (biased towards 0) by measurement error is represented as γ.

gamma.svg

The relationship between γ and β1 is characterized by κ.

kappa1.svg

In more plain terms, this is:

kappa2.svg

κ is the reliability ratio.


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MeasurementErrorModels (last edited 2025-04-29 19:24:42 by DominicRicottone)