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: This article proposes a multivariate two-sample test that can be conveniently used in the high dimension low sample size setup, and investigates the performance of this test on several high-dimensional simulated and real data sets, and demonstrates its superiority over several other existing two- sample tests.
87 citations
TL;DR: In this article, a sample reuse method for dependent data, based on a cross between the block bootstrap and Richardson extrapolation, is proposed, where instead of simulating a same size resample by resampling blocks and placing them end-to-end, it analyses the blocks directly and employs a variant of Richardson extrapolated to adjust for block size.
Abstract: We suggest a sample reuse method for dependent data, based on a cross between the block bootstrap and Richardson extrapolation. Instead of simulating a same size resample by resampling blocks and placing them end to end, it analyses the blocks directly and employs a variant of Richardson extrapolation to adjust for block size. A simple empirical rule, also based on Richardson extrapolation, is suggested for empirically selecting the block size. Performance in the contexts of distribution and bias estimation is discussed via theoretical analysis and numerical simulation.
87 citations
TL;DR: In this paper, an influence weight is assigned to each predictor in the conditioning set with the aim of identifying nearest neighbours that represent the conditional dependence in an improved manner, and the workability of the proposed modification is tested using synthetic data from known linear and nonlinear models and its applicability is illustrated through an example where daily rainfall is downscaled over 15 stations near Sydney, Australia using a predictor set consisting of selected large-scale atmospheric circulation variables.
87 citations
01 Jan 2003
TL;DR: This work proposes a novel approach for identifying the "most unusual" samples in a data set, based on a resampling of data attributes, which produces a "background class" and then binary classification is used to distinguish the original training set from the background.
Abstract: We propose a novel approach for identifying the 'most unusual' samples in a data set, based on a resampling of data attributes. The resampling produces a 'background class' and then binary classification is used to distinguish the original training set from the background. Those in the training set that are most like the background (i e, most unlike the rest of the training set) are considered anomalous. Although by their nature, anomalies do not permit a positive definition (if I knew what they were, I wouldn't call them anomalies), one can make 'negative definitions' (I can say what does not qualify as an interesting anomaly). By choosing different resampling schemes, one can identify different kinds of anomalies. For multispectral images, anomalous pixels correspond to locations on the ground with unusual spectral signatures or, depending on how feature sets are constructed, unusual spatial textures.
87 citations
01 Dec 2009
TL;DR: This paper investigates the important case of resampling detection in re-compressed JPEG images and shows how blocking artifacts of the previous compression step can help to increase the otherwise drastically reduced detection performance in JPEG compressed images.
Abstract: Resampling detection has become a standard tool in digital image forensics. This paper investigates the important case of resampling detection in re-compressed JPEG images. We show how blocking artifacts of the previous compression step can help to increase the otherwise drastically reduced detection performance in JPEG compressed images. We give a formulation on how affine transformations of JPEG compressed images affect state-of-the-art resampling detectors and derive a new efficient detection variant, which better suits this relevant detection scenario. The principal appropriateness of using JPEG pre-compression artifacts for the detection of resampling in re-compressed images is backed with experimental evidence on a large image set and for a variety of different JPEG qualities.
86 citations