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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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Journal ArticleDOI
TL;DR: The particle EIS method as mentioned in this paper is based on non-standard resampling weights that take into account the construction of the importance sampler as a sequential approximation to the state smoothing density.

34 citations

Journal ArticleDOI
TL;DR: Zhao & Liu (2014) argue that the pattern of the landscape process to be modeled determines the results of the resampling method, and state that the critical spatial resolution in scaling exercises follows a power-law function of the study region extent.
Abstract: Efforts to deduce the appropriate scales of ecosystem functions and how patterns change with scale have a long history in ecology and landscape ecology (Levin, 1992; O’Neill et al., 1996). Ecosystem function models are critical to predicting ecosystem responses to global change, but are limited by the technical challenges of model–data synthesis. Accurately relating phenomena across multiple scales is an important challenge in ecological modeling, as information is lost when converting between scales of analysis. Researchers must determine how much information is necessary to preserve the landscape signature of the ecological processes under study. Zhao & Liu (2014) sought to determine the appropriate spatial resolution for categorical land cover data to use in regional-scale models of carbon dynamics, and compared the use of two common categorical data resampling methods: majority (MR) and nearest neighbor (NNR). Their analysis of the NNR method showed a power-law relationship between study extent and grain, but results from MR method showed a different relationship, suggesting that the resampling method drove the results. Zhao & Liu (2014) concluded the NNR method to be superior and reported the MR approach produced ‘devastatingly deficient’ results. We discuss the lack of robustness of their power-law relationship by analyzing the configuration and composition of simulated landscapes subjected to different resampling methods. The authors stated that NNR is clearly preferential to the MR method because NNR preserves uncommon land cover types. They support their use of NNR by mis-citing Cain et al. (1997). Zhao & Liu (2014) state that the critical spatial resolution in scaling exercises follows a power-law function of the study region extent. We argue that the pattern of the landscape process to be modeled determines the results of the resampling method. We illustrate, using a simple simulated landscape, how the effect of resampling algorithm is related to the proportion of landscape within each land cover class and the spatial configuration (clumpiness)

34 citations

Journal Article
TL;DR: Through Monte Carlo simulations for a two target example, these four filters are compared to each other and to the approach of using one IMMPDA filter per target track and show that each of the four novel filters clearly outperforms the IMMJPDA approach.
Abstract: The problem of maintaining tracks of multiple maneuvering targets from unassociated measurements is formu- lated as a problem of estimating the hybrid state of a Markov jump linear system from measurements made by a descriptor system with independent, identically distributed (i.i.d.) stochastic coefficients. This characterization is exploited to derive the exact equation for the Bayesian recursive filter, to develop two novel Sampling Importance Resampling (SIR) type particle filters, and to derive approximate Bayesian filters which use for each target one Gaussian per maneuver mode. The two approximate Bayesian filters are a compact and a track-coalescence avoiding version of Interacting Multiple Model Joint Probababilistic Data Association (IMMJPDA). The relation of each of the four novel filter algorithms with literature is well explained. Through Monte Carlo simulations for a two target example, these four filters are compared to each other and to the approach of using one IMMPDA filter per target track. The Monte Carlo simulation results show that each of the four novel filters clearly outperforms the IMMPDA approach. The results also show under which conditions the IMMJPDA type filters perform close to exact Bayesian filtering, and under which conditions not.

34 citations

Journal ArticleDOI
TL;DR: In this article, the authors describe accurate analytic approximations to permutations of score statistics, including novel approaches for Pearson's correlation, and summed score statistics that have good performance for even relatively small sample sizes.
Abstract: SUMMARY Resampling-based expression pathway analysis techniques have been shown to preserve type I error rates, in contrast to simple gene-list approaches that implicitly assume the independence of genes in ranked lists. However, resampling is intensive in computation time and memory requirements. We describe accurate analytic approximations to permutations of score statistics, including novel approaches for Pearson’s correlation, and summed score statistics, that have good performance for even relatively small sample sizes. Our approach preserves the essence of permutation pathway analysis, but with greatly reduced computation. Extensions for inclusion of covariates and censored data are described, and we test the performance of our procedures using simulations based on real datasets. These approaches have been implemented in the new R package safeExpress.

34 citations

Journal ArticleDOI
TL;DR: Two versions of this test are introduced: the first gives a graphical interpretation of the test results in the original space of the functions and the second immediately offers a post-hoc test by identifying the significant pair-wise differences between groups.
Abstract: A new functional ANOVA test, with a graphical interpretation of the result, is presented. The test is an extension of the global envelope test introduced by Myllymaki et al. (2017, Global envelope tests for spatial processes, J. R. Statist. Soc. B 79, 381-404, doi: 10.1111/rssb.12172). The graphical interpretation is realized by a global envelope which is drawn jointly for all samples of functions. If a mean function computed from the empirical data is out of the given envelope, the null hypothesis is rejected with the predetermined significance level $\alpha$. The advantages of the proposed one-way functional ANOVA are that it identifies the domains of the functions which are responsible for the potential rejection. We introduce two versions of this test: the first gives a graphical interpretation of the test results in the original space of the functions and the second immediately offers a post-hoc test by identifying the significant pair-wise differences between groups. The proposed tests rely on discretization of the functions, therefore the tests are also applicable in the multidimensional ANOVA problem. In the empirical part of the article, we demonstrate the use of the method by analyzing fiscal decentralization in European countries.

34 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20251
20242
2023377
2022759
2021275
2020279