Topic
Resampling
About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.
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TL;DR: It is demonstrated that, although robust estimation and resampling variable selection are computationally complex procedures, they can combine both techniques for superior results using modest computational resources.
37 citations
TL;DR: Evaluated methods for estimating the exact significance level for including or excluding a covariate during model building found that for sparse as well as dense data, the first-order condition estimation methods yielded the best results while the second-order method performs somewhat better for sparse data.
Abstract: Purpose. One of the main objectives of the nonlinear mixed effects modeling is to provide rational individualized dosing strategies by explaining the interindividual variability using intrinsic and/or extrinsic factors (covariates). The aim of the current study was to evaluate, using computer simulations and real data, methods for estimating the exact significance level for including or excluding a covariate during model building.
37 citations
37 citations
TL;DR: The surrogate data generating algorithms AAFT, IAAFT and STAP, as well as a residual-based bootstrap algorithm, used for the randomization or bootstrap test for nonlinearity, are reviewed and their performance is compared using different nonlinear statistics for the test.
Abstract: The validity of any test for nonlinearity based on resampling techniques depends heavily on the consistency of the generated resampled data to the null hypothesis of linear stochastic process. The surrogate data generating algorithms AAFT, IAAFT and STAP, as well as a residual-based bootstrap algorithm, all used for the randomization or bootstrap test for nonlinearity, are reviewed and their performance is compared using different nonlinear statistics for the test. The simulations on linear and nonlinear stochastic systems, as well as chaotic systems, reveals a variation in the test outcome with the algorithm and statistic. Overall, the bootstrap algorithm led to smallest test power whereas the STAP algorithm gave consistently good results in terms of size and power of the test. The performance of the nonlinearity test with the resampling techniques is evaluated on volume and return time series of international stock exchange indices.
37 citations
TL;DR: A new method for directly estimating the AUC in the setting of verification bias based on U-statistics and inverse probability weighting (IPW) is developed and it is shown that the new estimator is equivalent to the empirical AUC derived from the bias-corrected ROC curve arising from the IPW approach.
Abstract: The area under a receiver operating characteristic (ROC) curve (AUC) is a commonly used index for summarizing the ability of a continuous diagnostic test to discriminate between healthy and diseased subjects. If all subjects have their true disease status verified, one can directly estimate the AUC nonparametrically using the Wilcoxon statistic. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Because estimators of the AUC based only on verified subjects are typically biased, it is common to estimate the AUC from a bias-corrected ROC curve. The variance of the estimator, however, does not have a closed-form expression and thus resampling techniques are used to obtain an estimate. In this paper, we develop a new method for directly estimating the AUC in the setting of verification bias based on U-statistics and inverse probability weighting (IPW). Closed-form expressions for the estimator and its variance are derived. We also show that the new estimator is equivalent to the empirical AUC derived from the bias-corrected ROC curve arising from the IPW approach.
37 citations