Model selection measures such as hold-out set error, cross-validation error, leave-one-out error etc. are used to evaluate the performance of a classification algorithm on a given data set. To get an accurate estimate of the performance it is important that we choose the appropriate model selection measure or evaluation measure for the setting of interest. In this paper, we describe in brief a ...

Robust estimators of the variogram can be used to identify a random function that describes the background variation of a soil property with the minimum influence of contaminants from a quasi point process. A method is proposed to exploit this to identify data which are best treated as a realization of a quasi point process rather than of a continuous random function for purposes of spatial ana...

A new class of model-assisted estimators based on local polynomial regression is suggested. The estimators are weighted linear combinations of study variables, in which the weights are calibrated to known control totals. The es-timators are asymptotically design-unbiased and consistent under mild assumptions , and we provide a consistent estimator for the design mean squared error. Bandwidth se...

The reliability of induced classification trees is most often evaluated by means of the error rate. Whether computed on test data or through cross-validation, this error rate is suited for classification purposes. We claim that it is, however, a partial indicator only of the quality of the knowledge provided by trees and that there is a need for additional indicators. For example, the error rat...

[1] This study uses a Bayesian approach to merge ensemble seasonal climate forecasts generated by multiple climate models for better probabilistic and deterministic forecasting. Within the Bayesian framework, the climatological distribution of the variable of interest serves as the prior, and the likelihood function is developed with a weighted linear regression between the climate model hindca...

resolution of binary mixtures of theophylline (theo), montelukast (mkst) and loratadine (lora) with minimum sample pretreatment and without analyte separation has been successfully achieved by multivariate spectrophotometric calibration, together with partial least-squares (pls-1), principal component regression (pcr) and hybrid linear analysis (hla). data of analysis were obtained from uv–vis ...

Robust estimators of the prediction error of a linear model are proposed. The estimators are based on the resampling techniques cross-validation and bootstrap. The robustness of the prediction error estimators is obtained by robustly estimating the regression parameters of the linear model and by trimming the largest prediction errors. To avoid the recalculation of timeconsuming robust regressi...

Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have been proposed for estimating the error rates of classiers. The rationale behind the various estimators and the causes of the sometimes con BLOCKINicting claims regarding their bias and precision are explored in this paper. The biases and variances of each of the estimators are examined empirically....

In parametric estimation of covariance function of Gaussian processes, it is often the case that the true covariance function does not belong to the parametric set used for estimation. This situation is called the misspecified case. In this case, it has been shown that, for irregular spatial sampling of observation points, Cross Validation can yield smaller prediction errors than Maximum Likeli...