2 This paper proposes a new framework to evaluate the performance of correcting systematic biases 3 commonly latent in vehicle counts measured at freeway loop detectors. Through the proposed framework, 4 one can evaluate the performance of bias-correction by evaluating the legitimacy of density estimates 5 generated with bias-corrected counts. To test this framework, traffic data for a 1.2km-lo...

This discussion paper is/has been under review for the journal Earth System Dynamics (ESD). Please refer to the corresponding final paper in ESD if available. Abstract Statistical bias correction is commonly applied within climate impact modeling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions ge...

The bootstrap has become a popular method for exploring model (structure) uncertainty. Our experiments with artificial and realworld data demonstrate that the graphs learned from bootstrap samples can be severely biased towards too complex graphical models. Accounting for this bias is hence essential, e.g., when exploring model uncertainty. We find that this bias is intimately tied to (well-kno...

We propose a computationally efficient approximation for the double bootstrap bias adjustment factor without using the inner bootstrap loop. The approximation converges in probability to the population bias correction factor. We study the finite sample properties of the approximation in the context of a linear instrumental variable model. In identified versions of the model considered in our Mo...

Previous longitudinal studies have demonstrated the importance of measuring stability of risk factors over time to correct for attenuation bias. The present aim was to assess the stability of scores for eight psychometric scales over a 2-yr. period and whether stability differed by socioeconomic position. Baseline data were collected during 2003-2004 from 1,007 men and women ages 45 to 69 years...

This paper empirically compares six background correction methods aimed at removing unspecific background noise of the overall signal level measured by a scanner across microarrays. Using three published cDNA microarray datasets we investigated the effect of background correction on cancer classification in terms of the predictive performance of two classifiers (k-NN and support vector machine ...

In studies to assess the accuracy of a screening test, often definitive disease assessment is too invasive or expensive to be ascertained on all the study subjects. Although it may be more ethical or cost effective to ascertain the true disease status with a higher rate in study subjects where the screening test or additional information is suggestive of disease, estimates of accuracy can be bi...

BACKGROUND A common feature of diagnostic research is that results for a diagnostic gold standard are available primarily for patients who are positive for the test under investigation. Data from such studies are subject to what has been termed "verification bias". We evaluated statistical methods for verification bias correction when there are few false negatives. METHODS A simulation study ...

• By numerically comparing a variable-selection method using the crude AIC with those using the bias-corrected AICs, we find out knowledge about what kind of bias correction gives a positive effect to variable selection under model misspecification. Actually, since all the variable-selection methods considered in this paper asymptotically choose the same model as the best model, we conduct nume...

This paper considers the small sample properties of the mean group estimator of the long-run coefficients in dynamic heterogeneous panels, and using Monte Carlo techniques examines the effectiveness of a number of alternative bias-correction procedures in reducing the small sample bias of these estimators. Four different biascorrected estimators of the long-run coefficients are considered. A “n...