Titel Deelnemers "Korte inhoud" "Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results" "Sandra STREUKENS, Sara LEROI-WERELDS" "Statistical inference, which relies on bootstrapping in partial least squares structural equation modeling (PLS-SEM), lies at the heart of developing practically relevant and academically rigorous theory. Inspection of PLS-SEM applications in European management research reveals that there is still much to be gained in terms of bootstrapping. This paper suggests several bootstrapping best practices and demonstrates how to conduct them for frequently encountered, yet often ignored, PLS-SEM situations such as the assessment of (non) direct effects, the comparison of effects, and the evaluation of the coefficient of determination." "Evaluating of bootstrap procedures for fMRI data" "Sanne Roels, Tom Loeys, Beatrijs Moerkerke" "Over the last decade the bootstrap procedure is gaining popularity in the statistical analysis of neuroimaging data. This powerful procedure can be used for example in the non-parametric analysis of neuro-imaging data. As fMRI data are complexly structured with both temporal and spatial dependencies, such bootstrap procedures may indeed offer an elegant solution. However, a thorough investigation on the most appropriate bootstrapping procedure for fMRI data has to our knowledge never been performed. Friman and Westin (2005) showed that a bootstrap procedure based on pre-whitening the temporal structure of fMRI time series is superior to procedures based on wavelets or Fourier decomposition of the signal, especially in the case of blocked fMRI designs. For time-series, several bootstrap schemes can be exploited though (see e.g. Lahiri, 2003). For the re-sampling of residuals from general linear models fitted on fMRI data, we examine more specifically here the differences between 1) bootstrapping pre-whitened residuals which are based on parametric assumptions of the temporal structure, 2) a blocked bootstrapping which avoids making such assumptions (with several variants like the circular bootstrap,. . . ), and 3) a combination of both bootstrap procedures. We furthermore explore whether the bootstrap procedures is best applied before or after smoothing the volume of interest. Based on real data and simulation studies, we discuss the temporal and spatial properties of the bootstrapped volumes for all possible combinations and nd interesting differences." "Exact Permutation and Bootstrap Distribution of Generalized Pairwise Comparisons Statistics" "William N. Anderson, Johan VERBEECK" "To analyze multivariate outcomes in clinical trials, several authors have suggested generalizations of the univariate Mann-Whitney test. As the Mann-Whitney statistic compares the subjects' outcome pairwise, the multivariate generalizations are known as generalized pairwise comparisons (GPC) statistics. For GPC statistics such as the net treatment benefit, the win ratio, and the win odds, asymptotic based or re-sampling tests have been suggested in the literature. However, asymptotic methods require a sufficiently high sample size to be accurate, and re-sampling methods come with a high computational burden. We use graph theory notation to obtain closed-form formulas for the expectation and the variance of the permutation and bootstrap sampling distribution of the GPC statistics, which can be utilized to develop fast and accurate inferential tests for each of the GPC statistics. A simple example and a simulation study demonstrate the accuracy of the exact permutation and bootstrap methods, even in very small samples. As the time complexity is O(N-2), where N is the total number of patients, the exact methods are fast. In situations where asymptotic methods have been used to obtain these variance matrices, the new methods will be more accurate and equally fast. In situations where bootstrap has been used, the new methods will be both more accurate and much faster." "The nonparametric bootstrap for the current status model" "Piet Groeneboom, Kim HENDRICKX" "It has been proved that direct bootstrapping of the nonparametric maximum likelihood estimator (MLE) of the distribution function in the current status model leads to inconsistent confidence intervals. We show that bootstrapping of functionals of the MLE can however be used to produce valid intervals. To this end, we prove that the bootstrapped MLE converges at the right rate in the L-p-distance. We also discuss applications of this result to the current status regression model." "Analyzing the variations in cost-efficiency of marine cage lobster aquaculture in Vietnam : a two-stage bootstrap DEA approach" "Au Ton Nu Hai, The Bui Dung" "How to bootstrap fMRI data?" "Sanne Roels, Tom Loeys, Beatrijs Moerkerke" "Over the last decade the bootstrap procedure is gaining popularity in the statistical analysis of neuro- imaging data. As fMRI data are complexly structured with both temporal and spatial dependencies, such bootstrap procedures may indeed offer an elegant and powerful solution. We provide a thorough investigation on the most appropriate bootstrapping procedure for fMRI data. Friman and Westin (2005, NeuroImage) showed that a bootstrap procedure based on pre-whitening the temporal structure of fMRI time series is superior to procedures based on wavelets or Fourier de- composition of the signal, especially in the case of blocked fMRI designs. Several bootstrap schemes can be exploited though for the re-sampling of residuals from a fitted general linear model. We examine here the differences between 1) bootstrapping pre-whitened residuals which are based on parametric as- sumptions of the temporal structure, 2) a blocked bootstrapping which avoids making such assumptions (with several variants like the circular bootstrap,...), and 3) a combination of both bootstrap procedures. Because the dependence in the residuals of a misspecified GLM might differ in blocked and event-related fMRI designs we investigated the bootstrapping schemes for both design types. Based on real data and simulation studies, we discuss the temporal and spatial properties of the different bootstrap procedures." "On the Added Value of Bootstrap Analysis for K-Means Clustering" "Joeri Hofmans, Eva Ceulemans, Douglas Steinley, Iven Van Mechelen" "Because of its deterministic nature, K-means does not yield confidence information about centroids and estimated cluster memberships, although this could be useful for inferential purposes. In this paper we propose to arrive at such information by means of a non-parametric bootstrap procedure, the performance of which is tested in an extensive simulation study. Results show that the coverage of hyper-ellipsoid bootstrap confidence regions for the centroids is in general close to the nominal coverage probability. For the cluster memberships, we found that probabilistic membership information derived from the bootstrap analysis can be used to improve the cluster assignment of individual objects, albeit only in the case of a very large number of clusters. However, in the case of smaller numbers of clusters, the probabilistic membership information still appeared to be useful as it indicates for which objects the cluster assignment resulting from the analysis of the original data is likely to be correct; hence, this information can be used to construct a partial clustering in which the latter objects only are assigned to clusters." "Robust bootstrap procedures for the chain-ladder method" "Kris Peremans, Pieter Segaert, Stefan Van Aelst" "Insurers are faced with the challenge of estimating the future reserves needed to handle historic and outstanding claims that are not fully settled. A well-known and widely used technique is the chain-ladder method, which is a deterministic algorithm. To include a stochastic component one may apply generalized linear models to the run-off triangles based on past claims data. Analytical expressions for the standard deviation of the resulting reserve estimates are typically difficult to derive. A popular alternative approach to obtain inference is to use the bootstrap technique. However, the standard procedures are very sensitive to the possible presence of outliers. These atypical observations, deviating from the pattern of the majority of the data, may both inflate or deflate traditional reserve estimates and corresponding inference such as their standard errors. Even when paired with a robust chain-ladder method, classical bootstrap inference may break down. Therefore, we discuss and implement several robust bootstrap procedures in the claims reserving framework and we investigate and compare their performance on both simulated and real data. We also illustrate their use for obtaining the distribution of one year risk measures." "Bootstrap confidence intervals in multi-level simultaneous component analysis" "Eva Ceulemans, Jeroen Stouten" "Multi-level simultaneous component analysis (MLSCA) was designed for the exploratory analysis of hierarchically ordered data. MLSCA specifies a component model for each level in the data, where appropriate constraints express possible similarities between groups of objects at a certain level, yielding four MLSCA variants. The present paper discusses different bootstrap strategies for estimating confidence intervals (CIs) on the individual parameters. In selecting a proper strategy, the main issues to address are the resampling scheme and the non-uniqueness of the parameters. The resampling scheme depends on which level(s) in the hierarchy are considered random, and which fixed. The degree of non-uniqueness depends on the MLSCA variant, and, in two variants, the extent to which the user exploits the transformational freedom. A comparative simulation study examines the quality of bootstrap CIs of different MLSCA parameters. Generally, the quality of bootstrap CIs appears to be good, provided the sample sizes are sufficient at each level that is considered to be random. The latter implies that if more than a single level is considered random, the total number of observations necessary to obtain reliable inferential information increases dramatically. An empirical example illustrates the use of bootstrap CIs in MLSCA." "Wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors" "Ingrid Van Keilegom" "We consider a heteroscedastic regression model in which some of the regression coefficients are zero but it is not known which ones. Penalized quantile regression is a useful approach for analyzing such data. By allowing different covariates to be relevant for modeling conditional quantile functions at different quantile levels, it provides a more complete picture of the conditional distribution of a response variable than mean regression. Existing work on penalized quantile regression has been mostly focused on point estimation. Although bootstrap procedures have recently been shown to be effective for inference for penalized mean regression, they are not directly applicable to penalized quantile regression with heteroscedastic errors. We prove that a wild residual bootstrap procedure for unpenalized quantile regression is asymptotically valid for approximating the distribution of a penalized quantile regression estimator with an adaptive L1 penalty and that a modified version can be used to approximate the distribution of L1-penalized quantile regression estimator. The new methods do not need to estimate the unknown error density function. We establish consistency, demonstrate finite sample performance, and illustrate the applications on a real data example. Some key words: Adaptive lasso; Confidence interval; Lasso; Penalized quantile regression; Wild bootstrap"