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Relevant bibliographies by topics / Reweighting method

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Academic literature on the topic 'Reweighting method'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Reweighting method"

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Shen, Zheyan, Peng Cui, Tong Zhang, and Kun Kunag. "Stable Learning via Sample Reweighting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5692–99. http://dx.doi.org/10.1609/aaai.v34i04.6024.

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We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of parameter estimation, resulting in instability of prediction results when training and test distributions do not match. In this paper we theoretically analyze this fundamental problem and propose a sample reweighting method that reduces collinearity among input variables. Our method can be seen as a pretreatment of data to improve the condition of design matrix, and it can then be combined with any standard learning method

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Nguyen, Nancy Duong, and Li-Chun Zhang. "An Appraisal of Common Reweighting Methods for Nonresponse in Household Surveys Based on the Norwegian Labour Force Survey and the Statistics on Income and Living Conditions Survey." Journal of Official Statistics 36, no. 1 (2020): 151–72. http://dx.doi.org/10.2478/jos-2020-0008.

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AbstractDespite increasing efforts during data collection, nonresponse remains sizeable in many household surveys. Statistical adjustment is hence unavoidable. By reweighting the design, weights of the respondents are adjusted to compensate for nonresponse. However, there is no consensus on how this should be carried out in general. Theoretical comparisons are inconclusive in the literature, and the associated simulation studies involve hypothetical situations not all equally relevant to reality. In this article we evaluate the three most common reweighting approaches in practice, based on rea

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Crompton, P. R. "Composite reweighting the Glasgow method for finite density QCD." Nuclear Physics B 619, no. 1-3 (2001): 499–506. http://dx.doi.org/10.1016/s0550-3213(01)00519-3.

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Ghoufi, A., F. Goujon, V. Lachet, and P. Malfreyt. "Multiple histogram reweighting method for the surface tension calculation." Journal of Chemical Physics 128, no. 15 (2008): 154718. http://dx.doi.org/10.1063/1.2904460.

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LEE, HWEE KUAN, and YUTAKA OKABE. "MULTISPIN CODING TECHNIQUE FOR NONEQUILIBRIUM REWEIGHTING." International Journal of Modern Physics C 17, no. 02 (2006): 157–65. http://dx.doi.org/10.1142/s0129183106008844.

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We present the multispin coding for the nonequilibrium reweighting method of the Monte Carlo simulation, that was developed by the present authors. As an illustration, we treat the driven diffusive lattice gas model. We use the multispin coding technique both for the spin update and for the calculation of the histogram of incremental weights, which is needed in the calculation of nonequilibrium reweighting. All the operations are executed by the bitwise logical commands.

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Hainmueller, Jens. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies." Political Analysis 20, no. 1 (2012): 25–46. http://dx.doi.org/10.1093/pan/mpr025.

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This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance impr

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Ruther, Matt, Stefan Leyk, and Barbara Buttenfield. "Deriving Small Area Mortality Estimates Using a Probabilistic Reweighting Method." Annals of the American Association of Geographers 107, no. 6 (2017): 1299–314. http://dx.doi.org/10.1080/24694452.2017.1320213.

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Boronat, J., and J. Casulleras. "Sampling differences in quantum Monte Carlo: A generalized reweighting method." Computer Physics Communications 121-122 (September 1999): 466–67. http://dx.doi.org/10.1016/s0010-4655(99)00383-5.

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Yu, Caixia, Jingtao Zhao, Yanfei Wang, and Zhen Qiu. "Sparse diffraction imaging method using an adaptive reweighting homotopy algorithm." Journal of Geophysics and Engineering 14, no. 1 (2016): 26–40. http://dx.doi.org/10.1088/1742-2132/14/1/26.

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Shi, Lei, Gangrong Qu, and Qian Wang. "A Method of Reweighting the Sensing Matrix for Compressed Sensing." IEEE Access 9 (2021): 21425–32. http://dx.doi.org/10.1109/access.2021.3054807.

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Dissertations / Theses on the topic "Reweighting method"

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Chen, Ziyue. "Generalizing Results from Randomized Trials to Target Population via Weighting Methods Using Propensity Score." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503007759352248.

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Fang, Zhou. "Reweighting methods in high dimensional regression." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:26f8541a-9e2d-466a-84aa-e6850c4baba9.

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In this thesis, we focus on the application of covariate reweighting with Lasso-style methods for regression in high dimensions, particularly where p ≥ n. We apply a particular focus to the case of sparse regression under a-priori grouping structures. In such problems, even in the linear case, accurate estimation is difficult. Various authors have suggested ideas such as the Group Lasso and the Sparse Group Lasso, based on convex penalties, or alternatively methods like the Group Bridge, which rely on convergence under repetition to some local minimum of a concave penalised likelihood. We prop

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Donati, Luca [Verfasser]. "Reweighting methods for molecular dynamics / Luca Donati." Berlin : Freie Universität Berlin, 2019. http://d-nb.info/1186062649/34.

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Lin, Hsi-Ching, and 林錫慶. "New Methods for Query Expansion and Query Reweighting for Document Retrieval." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/84539972972055978983.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>93<br>In document retrieval systems, query terms play an important role which can affect the performance of document retrieval systems. The performance of document retrieval systems can be improved by using query terms expansion techniques and query terms reweighting techniques. In this thesis, we present two new methods for query terms expansion and query terms rewieghting. The first method chooses additional query terms for query expansion according to the degrees of importance of relevant terms and use fuzzy rules to infer their weights for document retrieval. The

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Books on the topic "Reweighting method"

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Boudreau, Joseph F., and Eric S. Swanson. Classical spin systems. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0020.

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The thermodynamic properties of spin systems are evaluated with Monte Carlo methods. A review of classical thermodynamics is followed by a discussion of critical exponents. The Monte Carlo method is then applied to the two-dimensional Ising model with the goal of determining the phase diagram for magnetization. Boundary conditions, the reweighting method, autocorrelation, and critical slowing down are all explored. Cluster algorithms for overcoming critical slowing down are developed next and shown to dramatically reduce autocorrelation. A variety of spin systems that illustrate first, second,

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John, Landt, ed. Reweighting a base population for a microsimulation model. National Centre for Social and Economic Modelling, Faculty of Management, University of Canberra, 1995.

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Kravtsov, Vladimir. Heavy-tailed random matrices. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.13.

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This article considers non-Gaussian random matrices consisting of random variables with heavy-tailed probability distributions. In probability theory heavy tails of distributions describe rare but violent events which usually have a dominant influence on the statistics. Furthermore, they completely change the universal properties of eigenvalues and eigenvectors of random matrices. This article focuses on the universal macroscopic properties of Wigner matrices belonging to the Lévy basin of attraction, matrices representing stable free random variables, and a class of heavy-tailed matrices obta

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Book chapters on the topic "Reweighting method"

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Binder, Kurt, and Dieter W. Heermann. "Cluster Algorithms and Reweighting Methods." In Monte Carlo Simulation in Statistical Physics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10758-1_4.

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Marques, Reinaldo, and Geir Storvik. "Reweighting Schemes Based on Particle Methods." In The Contribution of Young Researchers to Bayesian Statistics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02084-6_14.

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Bottaro, Sandro, Tone Bengtsen, and Kresten Lindorff-Larsen. "Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach." In Methods in Molecular Biology. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0270-6_15.

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"Reweighting methods." In A Guide to Monte Carlo Simulations in Statistical Physics. Cambridge University Press, 2005. http://dx.doi.org/10.1017/cbo9780511614460.008.

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Panagiotopoulos, Athanassios. "Gibbs Ensemble and Histogram Reweighting Grand Canonical Monte Carlo Methods." In Simulation Methods for Polymers. CRC Press, 2004. http://dx.doi.org/10.1201/9780203021255.pt6.

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"Gibbs Ensemble and Histogram Reweighting Grand Canonical Monte Carlo Methods." In Simulation Methods for Polymers. CRC Press, 2004. http://dx.doi.org/10.1201/9780203021255-15.

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Conference papers on the topic "Reweighting method"

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Xu Kejia, Tan Zhiying, and Chen Bin. "Reweighting recognition using kernel method." In 2011 3rd International Conference on Computer Research and Development (ICCRD). IEEE, 2011. http://dx.doi.org/10.1109/iccrd.2011.5764047.

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Xia, Hengren, Jeffrey D. MacQueen, and Jeremy J. Zimmerman. "Reweighting method for polynomial surface fitting." In SEG Technical Program Expanded Abstracts 1990. Society of Exploration Geophysicists, 1990. http://dx.doi.org/10.1190/1.1890294.

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Li, Anyi. "Reweighting method in finite density lattice QCD." In XXIVth International Symposium on Lattice Field Theory. Sissa Medialab, 2006. http://dx.doi.org/10.22323/1.032.0030.

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Ishikawa, Ken-Ichi. "Testing reweighting method for truncated Overlap fermions." In 31st International Symposium on Lattice Field Theory LATTICE 2013. Sissa Medialab, 2014. http://dx.doi.org/10.22323/1.187.0026.

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EJIRI, SHINJI. "REMARKS ON THE REWEIGHTING METHOD IN THE CHEMICAL POTENTIAL DIRECTION." In Proceedings of the SEWM2002 Meeting. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704498_0025.

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Ishikawa, Tomomi, Yasumichi Aoki, and Taku Izubuchi. "Improving chiral property of domain-wall fermions by reweighting method." In The XXVII International Symposium on Lattice Field Theory. Sissa Medialab, 2010. http://dx.doi.org/10.22323/1.091.0035.

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Liu, Shubao, Ke-Yue Zhang, Taiping Yao, et al. "Dual Reweighting Domain Generalization for Face Presentation Attack Detection." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/120.

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Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios. Previous methods treat each sample from multiple domains indiscriminately during the training process, and endeavor to extract a common feature space to improve the generalization. However, due to complex and biased data distribution, directly treating them equally will corrupt the generalization ability. To settle the issue, we propose a novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance betw

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IWAMI, Ryo, Shinji Ejiri, Kazuyuki Kanaya, Yoshiyuki Nakagawa, Takashi Umeda, and Dai Yamamoto. "Multipoint reweighting method and beta-functions for the calculation of QCD equation of state." In The 32nd International Symposium on Lattice Field Theory. Sissa Medialab, 2015. http://dx.doi.org/10.22323/1.214.0222.

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Zhang, Chuheng, Yuanqi Li, Xi Chen, Yifei Jin, Pingzhong Tang, and Jian Li. "DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00087.

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Fu, Yan, Steve C. Wang, Urmila M. Diwekar, and Kemal H. Sahin. "A Stochastic Optimization Application for Vehicle Structures." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34075.

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With the continuous improvement of powerful computers, vehicle structural designs have been addressed using computational methods, resulting in more efficient development of new vehicles. Most simulation-based optimization generates deterministic optimal designs without considering variability effects in modeling, simulation, and/or manufacturing. This paper presents an application of a new stochastic optimization method for vehicle side impact design. Nonlinear response surface models are employed for approximations of the side impact related test performance functions to conduct this study.

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