The estimators most widely used to evaluate the prediction error of a non-linear regression model are examined. An extensive simulation approach allowed the comparison of the performance of these estimators for different non-parametric methods, and with varying signal-to-noise ratio and sample size. Estimators based on resampling methods such as Leave-one-out, parametric and non-parametric Boot...

In this article, we obtain an importance resampling formula to reduce the amount of resampling necessary for the construction of bootstrap con dence regions. In the one-dimensional case, the formula reduces to that of Jones (1988) and Do & Hall (1991). However, the methods employed by previous authors are not tamed for direct generalization to multi-dimensional parameters. Therefore no formula ...

Data Envelopment Analysis (DEA) is a mathematical programming formulation based technique that provides an efficient frontier to suggest an estimate of the relative efficiency of each decision making unit (DMU) in a problem set. DEA is developed around the concept of evaluating the efficiency of a decision alternative based on its performance of creating outputs in means of input consumption. B...

Abstract The receiver operating characteristic (ROC) curve is defined as true positive rate versus false positive rate obtained by varying a decision threshold criterion. It has been widely used in medical science for its ability to measure the accuracy of diagnostic or prognostic tests. Mathematically speaking, ROC curve is the composition of survival function of one population with the quanti...

Though the common default maximum likelihood estimator used in structural equation modeling is predicated on the assumption of multivariate normality, applied researchers often find themselves with data clearly violating this assumption and without sufficient sample size to utilize distribution-free estimation methods. Fortunately, promising alternatives are being integrated into popular softwa...

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle cl...

Internal validity of a risk model can be studied efficiently with bootstrapping to assess possible optimism in model performance. Assumptions of the regular bootstrap are violated when the development data are clustered. We compared alternative resampling schemes in clustered data for the estimation of optimism in model performance. A simulation study was conducted to compare regular resampling...

The bootstrap method has become a widely used tool applied in diverse areas where results based on asymptotic theory are scarce. It can be applied, for example, for assessing the variance of a statistic, a quantile of interest or for significance testing by resampling from the null hypothesis. Recently, some approaches have been proposed in the biometrical field where hypothesis testing or mode...

It is a fact that p values are commonly used for inference in biomedical and other social fields of research. Unfortunately, the role of p value is very often misused and misinterpreted; that is why it has been recommended the use of resampling methods, like the bootstrap method, to calculate the confidence interval, which provides more robust results for inference than does p value. In this re...

Estimation of the generalization performance for classification within the medical applications domain is always an important task. In this study we focus on artificial neural network ensembles as the machine learning technique. We present a numerical comparison between five common resampling techniques: k-fold cross validation (CV), holdout, using three cutoffs, and bootstrap using five differ...