We present a novel method for evaluating grammatical error correction. The core of our method, which we call MaxMatch (M), is an algorithm for efficiently computing the sequence of phrase-level edits between a source sentence and a system hypothesis that achieves the highest overlap with the goldstandard annotation. This optimal edit sequence is subsequently scored using F1 measure. We test our...

Motivated by inferring cellular signaling networks using noisy flow cytometry data, we develop procedures to draw inference for Bayesian based on error-prone data. Two methods causal relationships between nodes in a network are proposed penalized estimation that account measurement error and encourage sparsity. We discuss consistency of the estimators an approach selecting tuning parameter meth...

We compare two consistent estimators of the parameter vector β of a general exponential family measurement error model with respect to their relative efficiency. The quasi score (QS) estimator uses the distribution of the regressor, the corrected score (CS) estimator does not make use of this distribution and is therefore more robust. However, if the regressor distribution is known, QS is asymp...

We consider a polynomial regression model, where the covariate is measured with Gaussian errors. The measurement error variance is supposed to be known. The covariate is normally distributed with known mean and variance. Quasi Score (QS) and Corrected Score (CS) are two consistent estimation methods, where the first makes use of the distribution of the covariate (structural method), while the l...

There are some errors on page 2 with the cutoff score for MCI based on MoCA score. Under Methodology, in the last paragraph—5th line, there is an error, " A MoCA score of 26 or less is considered as MCI " which should read as " A MoCA score of less than 26 (25 or less) is considered as MCI. " Under Statistical Analysis— line 9, there is another error, scores " ≤26 " which should be " <26, " and...

This study considers variance estimation when estimating the asymptotic variance of a propensity score matching estimator for the average treatment effect. We investigate the role of smoothing parameters in a variance estimator based on matching. We also study the properties of estimators using local linear estimation. Simulations demonstrate that large gains can be made in terms of mean square...

The linear measurement error model is an alternative to the classical regression model, in which we assume that the independent variables are subject to error. This assumption can cause statistical inferences and parameter estimators to di er dramatically from those obtained from the classical regression model. However, inferences may remain unchanged even though the independent variables are a...