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Relevant bibliographies by topics / Resampling

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Academic literature on the topic 'Resampling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Resampling"

1

Gentleman, Robert. "Resampling Methods." American Statistician 57, no. 1 (2003): 70. http://dx.doi.org/10.1198/tas.2003.s216.

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Ziegel, Eric. "Resampling Methods." Technometrics 48, no. 4 (2006): 576. http://dx.doi.org/10.1198/tech.2006.s443.

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Watson, Jane M. "Resampling withTinkerPlots." Teaching Statistics 35, no. 1 (2012): 32–36. http://dx.doi.org/10.1111/j.1467-9639.2012.00511.x.

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Reeves, Jane. "Resampling Stats." Teaching Statistics 17, no. 3 (1995): 101–3. http://dx.doi.org/10.1111/j.1467-9639.1995.tb00722.x.

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GOLDBERG, GERALD F. "RESAMPLING SIMULATION." Quality Engineering 6, no. 3 (1994): 375–78. http://dx.doi.org/10.1080/08982119408918734.

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Chernick, Michael R. "Resampling methods." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, no. 3 (2012): 255–62. http://dx.doi.org/10.1002/widm.1054.

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Pheatt, Charles B. "Resampling Stats: User Guide, Resampling: The New Statistics." Journal of Quality Technology 25, no. 4 (1993): 319–21. http://dx.doi.org/10.1080/00224065.1993.11979484.

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Peng, Anjie, Yadong Wu, and Xiangui Kang. "Revealing Traces of Image Resampling and Resampling Antiforensics." Advances in Multimedia 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/7130491.

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Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. In this paper, we proposed an effective and secure detector, which can simultaneously detect resampling and its forged resampling which is attacked by antiforensic schemes. We find that the interpolation operation used in the resampling and forged resampling makes these two kinds of image show different statistical behaviors from the unaltered images, especially in the high frequency domain. To reveal the traces left b

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Yan, Diqun, Yongkang Gong, and Tianyun Liu. "Antiforensics of Speech Resampling Using Dual-Path Strategy." Wireless Communications and Mobile Computing 2021 (January 15, 2021): 1–8. http://dx.doi.org/10.1155/2021/6640106.

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Resampling is an operation to convert a digital speech from a given sampling rate to a different one. It can be used to interface two systems with different sampling rates. Unfortunately, resampling may also be intentionally utilized as a postoperation to remove the manipulated artifacts left by pitch shifting, splicing, etc. To detect the resampling, some forensic detectors have been proposed. Little consideration, however, has been given to the security of these detectors themselves. To expose weaknesses of these resampling detectors and hide the resampling artifacts, a dual-path resampling

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Papalia, M. Fikar, Solimun Solimun, and Nurjannah Nurjannah. "COMPARISON OF RESAMPLING EFFICIENCY LEVELS OF JACKKNIFE AND DOUBLE JACKKNIFE IN PATH ANALYSIS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 2 (2023): 0807–18. http://dx.doi.org/10.30598/barekengvol17iss2pp0807-0818.

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The assumption of normality is often not fulfilled, this causes the estimation of the resulting parameters to be less efficient. The problem with assuming that normality is not satisfied can be overcome by resampling. The use of resampling allows data to be applied free of distributional assumptions. In this study, a research simulation was carried out by applying Jackknife resampling and Double Jackknife resampling in path analysis with the assumption that the normality of the residuals was not fulfilled and the number of resampling was set at 100 with the degree of closeness level of relatio

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Dissertations / Theses on the topic "Resampling"

1

Dodgson, Neil Anthony. "Image resampling." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240148.

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Hol, Jeroen D. "Resampling in particle filters." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2366.

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<p>In this report a comparison is made between four frequently encountered resampling algorithms for particle filters. A theoretical framework is introduced to be able to understand and explain the differences between the resampling algorithms. This facilitates a comparison of the algorithms based on resampling quality and on computational complexity. Using extensive Monte Carlo simulations the theoretical results are verified. It is found that systematic resampling is favourable, both in resampling quality and computational complexity.</p>

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Talbot, Justin F. "Importance Resampling for Global Illumination." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1021.pdf.

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Russell, Andrew Ian 1975. "Regular and irregular signal resampling." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/29761.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (p. 107-110).<br>In this thesis, we consider three main resampling problems. The first is the sampling rate conversion problem in which the input and output grids are both regularly spaced. It is known that the output signal is obtained by applying a time-varying filter to the input signal. The existing methods for finding the coefficients of this filter inherently tradeoff computational and memory requirements. Instead, we present a recursi

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Qi, Weinan. "On Resampling Schemes for Uniform Polytopes." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36057.

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The convex hull of a sample is used to approximate the support of the underlying distribution. This approximation has many practical implications in real life. For example, approximating the boundary of a finite set is used by many authors in environmental studies and medical research. To approximate the functionals of convex hulls, asymptotic theory plays a crucial role. Unfortunately, the asymptotic results are mostly very complicated. To address this complication, we suggest a consistent bootstrapping scheme for certain cases. Our resampling technique is used for both semi-parametric and

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鄧雅恩 and Nga-yan Fancy Tang. "Resampling tests for some survival models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226759.

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Obanubi, Olasunkanmi Olubunmi. "Particle filters with random resampling times." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/6339.

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The ability to analyse, interpret and make inferences about evolving dynamical systems is of great importance in different areas of the world we live in today. Examples of such areas include audio engineering, finance and econometrics. In general, the dynamical systems are not directly measureable and only incomplete observations, quite often deteriorated by the presence noise are available. This leads us to the main objective of stochastic filtering: the estimation of an evolving dynamical system whose trajectory is modelled by a stochastic process called the signal, given the information ava

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Tang, Nga-yan Fancy. "Resampling tests for some survival models /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23457338.

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Kohli, Jasraj. "An Empirical Analysis of Resampled Efficiency." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-042605-143547/.

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Wong, Oi-ling Irene, and 黃愛玲. "A study of Saddlepoint-based resampling methods." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31223667.

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Books on the topic "Resampling"

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Good, Phillip I. Resampling Methods. Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4757-3049-4.

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Good, Phillip I. Resampling Methods. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4757-3425-6.

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Lahiri, S. N. Resampling Methods for Dependent Data. Springer New York, 2003. http://dx.doi.org/10.1007/978-1-4757-3803-2.

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Srivastava, M. S. Resampling methods for imputing missing observations. University of Toronto, 1997.

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Srivastava, M. S. Resampling methods for imputing missing observations in regression models. University of Toronto, 1997.

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Bose, Arup, and Snigdhansu Chatterjee. U-Statistics, Mm-Estimators and Resampling. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2248-8.

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Chihara, Laura M., and Tim C. Hesterberg. Mathematical Statistics with Resampling and R. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119505969.

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1959-, Hesterberg Tim, ed. Mathematical statistics with resampling and R. Wiley, 2011.

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9

Lansdale, Robert C. Texture mapping and resampling for computer graphics. National Library of Canada = Bibliothèque nationale du Canada, 1991.

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10

Shasha, Dennis Elliott. Statistics is easy! 2nd ed. Morgan & Claypool Publishers, 2011.

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Book chapters on the topic "Resampling"

1

Suzuki, Joe. "Resampling." In Statistical Learning with Math and R. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7568-6_4.

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Beran, Jan, Yuanhua Feng, Sucharita Ghosh, and Rafal Kulik. "Resampling." In Long-Memory Processes. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35512-7_10.

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Sen, Rituparna, and Sourish Das. "Resampling." In Computational Finance with R. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-2008-0_15.

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Ruppert, David. "Resampling." In Springer Texts in Statistics. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4419-6876-0_10.

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Suzuki, Joe. "Resampling." In Statistical Learning with Math and Python. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7877-9_4.

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Ruppert, David. "Resampling." In Statistics and Data Analysis for Financial Engineering. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7787-8_6.

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Ruppert, David, and David S. Matteson. "Resampling." In Statistics and Data Analysis for Financial Engineering. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2614-5_6.

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8

Good, Phillip I. "Descriptive Statistics." In Resampling Methods. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4757-3425-6_1.

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Good, Phillip I. "Model Building." In Resampling Methods. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4757-3425-6_10.

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Good, Phillip I. "Which Statistic Should I Use?" In Resampling Methods. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4757-3425-6_11.

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Conference papers on the topic "Resampling"

1

Liu, Yujian, Yang Zhang, Tommi Jaakkola, and Shiyu Chang. "Correcting Diffusion Generation Through Resampling." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00832.

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Malandain, Grégoire, and Patrick Lemaire. "Surface Preserving Resampling of Labeled Images." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980702.

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Wu, Chia-Yu, Ying-Kai Chen, and Shih-Hsiang Hsu. "Silicon Photonic Swept-Source Optical Coherence Tomography Resampling." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.ath3b.7.

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The dispersion-related optical-clock resampling of a swept-source optical coherence tomography in a silicon platform is demonstrated on the main interferometer through Hilbert-transform for the axial resolution of 21.78 µm, which is limited by grating couplers.

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Stokes, John. "Resampling satellite scenes." In Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, edited by Heinrich Ebner, Christian Heipke, and Konrad Eder. SPIE, 1994. http://dx.doi.org/10.1117/12.182867.

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Friedrich, Tobias, Timo Kötzing, Francesco Quinzan, and Andrew M. Sutton. "Resampling vs Recombination." In the 14th ACM/SIGEVO Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3040718.3040723.

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Massey, Bart. "Fast perfect weighted resampling." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518395.

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Petrov, Mihail, and Manfred Glesner. "A Scalable Resampling Architecture." In IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference. IEEE, 2007. http://dx.doi.org/10.1109/glocom.2007.587.

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Eldon, John A. "Image Transformation And Resampling." In Medical Imaging II, edited by Roger H. Schneider and Samuel J. Dwyer III. SPIE, 1988. http://dx.doi.org/10.1117/12.968688.

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Murata, Masaya, Hidehisa Nagano, and Kunio Kashino. "Gaussian sum resampling filter." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402896.

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Segall, Andrew, and Aggelos Katsaggelos. "Resampling for Spatial Scalability." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312364.

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Reports on the topic "Resampling"

1

Ramos, Ernesto. Resampling Methods for Time Series. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198942.

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Chaganty, N. R. Large Deviations for Resampling Methods and Simulations. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada371375.

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Smith, A. F., and A. E. Gelfand. Bayes Theorem from a Sampling-Resampling Perspective. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada239515.

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Kraut, Shawn, and Jeffrey Krolik. Moving Target Depth Estimation for Passive Sonar, Using Sequential Resampling Techniques. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada405125.

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Nimeroff, Jeffrey S., Norman I. Badler, and Dimitri Metaxas. Texture Resampling while Ray-Tracing: Approximating the Convolution Region Using Caching. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290472.

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Miller-Chang, Yeng Nordman. On optimal block resampling for Gaussian-subordinated long-range dependent processes. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-1078.

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Doss, Hani. Statistical Inference for Coherent Systems from Partial Information and in Resampling Techniques in Survival Analysis. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada277382.

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Sinclair, Samantha, and Sally Shoop. Automated detection of austere entry landing zones : a “GRAIL Tools” validation assessment. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45265.

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The Geospatial Remote Assessment for Ingress Locations (GRAIL) Tools software is a geospatial product developed to locate austere entry landing zones (LZs) for military aircraft. Using spatial datasets like land classification and slope, along with predefined LZ geometry specifications, GRAIL Tools generates binary suitability filters that distinguish between suitable and unsuitable terrain. GRAIL Tools combines input suitability filters, searches for LZs at user‐defined orientations, and plots results. To refine GRAIL Tools, we: (a) verified software output; (b) conducted validation assessmen

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Abdelhay, Osama, Adam Shatnawi, Hasan Najadat, and Taghreed Altamimi. Protocol for a Scoped Systematic Review and Meta-Regression of Resampling Methods in Imbalanced Medical Datasets: Data-Level and Algorithm-Level Strategies in Clinical Prediction Models. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2025. https://doi.org/10.37766/inplasy2025.5.0026.

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