Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. Like generalized regression estimators, these estimators are weighted linear combinations of study variables, in which the weights are calibrated to known control totals, but the assumptions on the superpopulation model are consid...

A well-known result by Stein shows that regularized estimators with small bias often yield better estimates than unbiased estimators. In this paper, we adapt this spirit to model selection, and propose regularizing unbiased generalization error estimators for stabilization. We trade a small bias in a model selection criterion against a larger variance reduction which has the beneficial effect o...

k-fold cross validation is a popular practical method to get a good estimate of the error rate of a learning algorithm. Here, the set of examples is first partitioned into k equal-sized folds. Each fold acts as a test set for evaluating the hypothesis learned on the other k − 1 folds. The average error across the k hypotheses is used as an estimate of the error rate. Although widely used, espec...

is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...

is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...

is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...

This paper focuses on a bias variance decomposition analysis of a local learning algorithm, the nearest neighbor classiier, that has been extended with error correcting output codes. This extended algorithm often considerably reduces the 0-1 (i.e., classiication) error in comparison with nearest neighbor (Ricci & Aha, 1997). The analysis presented here reveals that this performance improvement ...

this study investigated the dynamic relationship between money, prices and output in a multivariate structure of casualty analysis in iran for the two period of 1969 to 2012 (entire period) and 1989 to 2012 (sub-period). this statistical framework has been projected for situations where causal links may have changed over the sample period. results of a three-variable vector error correction mod...