Simulation-Extrapolation Estimation in Parametric Measurement Error Models

Simulation - Extrapolation : the Measurement Error

Parametric Measurement Error Models

We describe a simulation-based method of inference for parametric measurement error models in which the measurement error variance is known or at least well estimated. The method entails adding additional measurement error in known increments to the data, computing estimates from the contaminated data, establishing a trend between these estimates and the variance of the added errors, and extrap...

Flexible parametric measurement error models.

Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect, the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference, we propose using flexible parametric models that can accommodate departures from standard parametric models. We use mixtures of norma...

Approximate Quasilikelihood Estimation in Measurement Error Models

Leonard A. Stefanski Department of Statistics North Carolina State University Raleigh, NC 27695 We consider quasllikelihood estimation with estimated parameters in the variance function when some of the predictors are measured with error. We review and extend four approaches to estimation in this problem, all of them based on small measurement error approximations. A taxonomy of the data sets l...

Restricted regression estimation in measurement error models

The problem of consistent estimation of the regression coefficients when some prior information about the regression coefficients is available is considered. Such prior information is expressed in the form of exact linear restrictions. The knowledge of covariance matrix of measurement errors that is associated with explanatory variables is used to construct the consistent estimators. Some consi...