The effect of systematic measurement errors on atmospheric CO2 inversions: a quantitative assessment C. Rödenbeck, T. J. Conway, and R. Langenfelds Max Planck Institute for Biogeochemistry, Jena, Germany NOAA Climate Monitoring and Diagnostics Laboratory, Boulder CO, USA Commonwealth Scientific and Industrial Research Organisation, Australia Received: 25 July 2005 – Accepted: 26 August 2005 – P...

Nonparametric regression provides a useful tool for exploring the association between the responses and covariates. In many applications, the actual values of the covariates are not known, but are measured with errors or through surrogates. In this paper, we present nonparametric regression techniques to study the association between the response and the underlying unobserved covariate. Deconvo...

As was shown recently, the measurement errors in regressors affect only the power of the rank test, but not its critical region. Noting that, we study the effect of measurement errors on R-estimators in linear model. It is demonstrated that while an R-estimator admits a local asymptotic bias, its bias surprisingly depends only on the precision of measurements and does neither depend on the chos...

One of the weak points of DEA (Data Envelopment Analysis) models indicated in literature [1,2] is their sensitivity to variable measurement errors. The occurrence of data interference, which is the basis of the productivity analysis, may distort the classification of the units and may cause misjudgement of their effectiveness. In the article the results of simulation concerning the DEA models s...

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The pap...

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The pap...

In this study, a simple 2-D measurement system based on optical design was developed to measure the motion errors of the linear guideway. Compared with the transitional methods about the linear guideway for measuring the motion errors, our proposed 2-D optical measurement system can simultaneously measure horizontal and vertical running straightness errors for the linear guideway. The performan...

In a measurement process, measurement data may involve gross errors, which significantly exceed true values, when a measurement quantity is measured repeatedly without significant changes in measurement conditions. If the suspicious data remained during data processing, it will cause incorrect assessment on measurement accuracy due to the distorted measurement results have been used. Correct id...