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: A novel method for resampling and enhancing image data using multidimensional adaptive filters is presented and clearly shows an improvement over conventional resampled techniques such as cubic spline interpolation and sinc interpolation.
49 citations
TL;DR: This article showed that a straightforward extrapolation of the bootstrap distribution obtained by resampling without replacement, as considered by Politis and Romano, leads to second-order correct confidence intervals, provided that the sampling size is chosen adequately.
Abstract: This paper shows that a straightforward extrapolation of the bootstrap distribution obtained by resampling without replacement, as considered by Politis and Romano, leads to second-order correct confidence intervals, provided that the resampling size is chosen adequately. We assume only that the statistic of interest Tn, suitably renormalized by a regular sequence, is asymptotically pivotal and admits an Edgeworth expansion on some differentiable functions. The results are extended to a corrected version of the moving-block bootstrap without replacement introduced by Kunsch for strong-mixing random fields. Moreover, we show that the generalized jackknife or the Richardson extrapolation of such bootstrap distributions, as considered by Bickel and Yahav, leads to better approximations.
49 citations
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TL;DR: This paper proposes three new schemes that considerably improve the performance of the original PMC formulation by allowing for better exploration of the space of unknowns and by selecting more adequately the surviving samples.
Abstract: Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating functions for the samples and the avoidance of the sample degeneracy. In this paper, we propose three new schemes that considerably improve the performance of the original PMC formulation by allowing for better exploration of the space of unknowns and by selecting more adequately the surviving samples. A theoretical analysis is performed, proving the superiority of the novel schemes in terms of variance of the associated estimators and preservation of the sample diversity. Furthermore, we show that they outperform other state of the art algorithms (both in terms of mean square error and robustness w.r.t. initialization) through extensive numerical simulations.
49 citations
TL;DR: An overview of how resampling methods may be applied to linear models of ontogenetic trajectories of landmark-based geometric morphometric data, to extract in- formation about ontogeny is presented.
Abstract: Keywords: ontogeny shape permutation bootstrapping resampling MANCOVA Abstract Comparative studies of ontogenies play a crucial role in the understanding of the processes of mor- phological diversification. These studies have benefited from the appearance of new mathematical and statistical tools, including geometric morphometrics, resampling statistics and general linear models. This paper presents an overview of how resampling methods may be applied to linear models of ontogenetic trajectories of landmark-based geometric morphometric data, to extract in- formation about ontogeny. That information can be used to test hypotheses about the changes (or dierences) in rate, direction, duration and starting point of ontogenetic trajectories that led to the observed patterns of morphological diversification.
49 citations
TL;DR: This work investigates a general resampling approach (BI-SS) that combines bootstrap imputation and stability selection, the latter of which was developed for fully observed data and can be applied to a wide range of settings.
Abstract: In the presence of missing data, variable selection methods need to be tailored to missing data mechanisms and statistical approaches used for handling missing data. We focus on the mechanism of missing at random and variable selection methods that can be combined with imputation. We investigate a general resampling approach (BI-SS) that combines bootstrap imputation and stability selection, the latter of which was developed for fully observed data. The proposed approach is general and can be applied to a wide range of settings. Our extensive simulation studies demonstrate that the performance of BI-SS is the best or close to the best and is relatively insensitive to tuning parameter values in terms of variable selection, compared with several existing methods for both low-dimensional and high-dimensional problems. The proposed approach is further illustrated using two applications, one for a low-dimensional problem and the other for a high-dimensional problem.
49 citations