Готові списки джерел за темами / Empirical methods

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Добірка наукової літератури з теми "Empirical methods"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 31 січня 2023

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Статті в журналах з теми "Empirical methods"

Lindley, D. V., J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Mathematical Gazette 74, no. 467 (March 1990): 91. http://dx.doi.org/10.2307/3618894.

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Bagghi, Parthasarathy, J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Journal of the American Statistical Association 86, no. 413 (March 1991): 244. http://dx.doi.org/10.2307/2289739.

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Angus, John E. "Empirical Bayes Methods." Technometrics 33, no. 2 (May 1991): 243–45. http://dx.doi.org/10.1080/00401706.1991.10484821.

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Stephenson, W. Robert. "Empirical Bayes Methods." Journal of Quality Technology 22, no. 3 (July 1990): 249–50. http://dx.doi.org/10.1080/00224065.1990.11979250.

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Young, Karen, J. Maritz, and T. Lwin. "Empirical Bayes Methods." Applied Statistics 41, no. 3 (1992): 604. http://dx.doi.org/10.2307/2348097.

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Denham, Mike, J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Statistician 39, no. 1 (1990): 97. http://dx.doi.org/10.2307/2348214.

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Schneider-Mayerson, Matthew, Alexa Weik von Mossner, and W. P. Małecki. "Empirical Ecocriticism: Environmental Texts and Empirical Methods." ISLE: Interdisciplinary Studies in Literature and Environment 27, no. 2 (2020): 327–36. http://dx.doi.org/10.1093/isle/isaa022.

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ter Beek, Maurice H., and Alessio Ferrari. "Empirical Formal Methods: Guidelines for Performing Empirical Studies on Formal Methods." Software 1, no. 4 (September 24, 2022): 381–416. http://dx.doi.org/10.3390/software1040017.

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Анотація:

Empirical studies on formal methods and tools are rare. In this paper, we provide guidelines for such studies. We mention their main ingredients and then define nine different study strategies (usability testing, laboratory experiments with software and human subjects, case studies, qualitative studies, surveys, judgement studies, systematic literature reviews, and systematic mapping studies) and discuss for each of them their crucial characteristics, the difficulties of applying them to formal methods and tools, typical threats to validity, their maturity in formal methods, pointers to external guidelines, and pointers to studies in other fields. We conclude with a number of challenges for empirical formal methods.

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Wolstein, Benjamin. "Five Empirical Psychoanalytic Methods." Contemporary Psychoanalysis 26, no. 2 (April 1990): 237–56. http://dx.doi.org/10.1080/00107530.1990.10746657.

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Laird, Nan M., and Thomas A. Louis. "Empirical Bayes Ranking Methods." Journal of Educational Statistics 14, no. 1 (March 1989): 29–46. http://dx.doi.org/10.3102/10769986014001029.

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Ranking problems arise in setting priorities for investigations, in providing a simple summary of performance, in comparing objects in a manner robust to measurement scale, and in a wide variety of other applications. Commonly, rankings are computed from measurements that depend on the true attribute. Using the Gaussian model, we propose and compare methods for using these measurements to estimate the ranks of the underlying attributes and show that those based on an empirical Bayes model produce estimates that differ from ranking observed data. These differences result both from the effect of shrinking posterior means towards a common value by an amount that depends on the precision of individual measurements and from the Bayes processing of the posterior distribution to produce estimates that account for the uncertainty in the distribution of the ranks. We illustrate different ranking methods using data on school achievement reported by Aitkin and Longford (1986) . Mathematical and empirical results highlight the importance of using appropriate ranking methods and identify issues requiring further research.

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Дисертації з теми "Empirical methods"

Luta, Gheorghe Sen Pranab Kumar Koch Gary G. "Empirical likelihood-based adjustment methods." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,502.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.
Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics." Discipline: Biostatistics; Department/School: Public Health.

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Zawadzki, Erik P. "Multiagent learning and empirical methods." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2480.

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Many algorithms exist for learning how to act in a repeated game and most have theoretical guarantees associated with their behaviour. However, there are few experimental results about the empirical performance of these algorithms, which is important for any practical application of this work. Most of the empirical claims in the literature to date have been based on small experiments, and this has hampered the development of multiagent learning (MAL) algorithms with good performance properties. In order to rectify this problem, we have developed a suite of tools for running multiagent experiments called the Multiagent Learning Testbed (MALT). These tools are designed to facilitate running larger and more comprehensive experiments by removing the need to code one-off experimental apparatus. MALT also provides a number of public implementations of MAL algorithms—hopefully eliminating or reducing differences between algorithm implementations and increasing the reproducibility of results. Using this test-suite, we ran an experiment that is unprecedented in terms of the number of MAL algorithms used and the number of game instances generated. The results of this experiment were analyzed by using a variety of performance metrics—including reward, maxmin distance, regret, and several types of convergence. Our investigation also draws upon a number of empirical analysis methods. Through this analysis we found some surprising results: the most surprising observation was that a very simple algorithm—one that was intended for single-agent reinforcement problems and not multiagent learning— performed better empirically than more complicated and recent MAL algorithms.

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Fevang, Rune, and Arne Bergene Fossaa. "Empirical evaluation of metric indexing methods." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8902.

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Metric indexing is a branch of search technology that is designed for search non-textual data. Examples of this includes image search (where the search query is an image), document search (finding documents that are roughly equal) to search in high-dimensional Euclidean spaces. Metric indexing is based on the theory of metric spaces, where the only thing known about a set of objects is the distance between them (defined by a metric distance function). A large number of methods have been proposed to solve the metric indexing problem. In this thesis, we have concentrated on new approaches to solving these problems, as well as combining existing methods to create better ones. The methods studied in this thesis include D-Index, GNAT, EMVP-Forest, HC, SA-Tree, SSS-Tree, M-Tree, PM-Tree, M*-Tree and PM*-Tree. These have all been implemented and tested against each other to find strengths and weaknesses. This thesis also studies a group of indexing methods called hybrid methods which combines tree-based methods (like SA-Tree, SSS-tree and M-Tree), with pivoting methods (like AESA and LAESA). The thesis also proposes a method to create hybrid trees from existing trees by using features in the programming language. Hybrid methods have been shown in this thesis to be very promising. While they may have a considerable overhead in construction time,CPU usage and/or memory usage, they show large benefits in reduced number of distance computations. We also propose a new way of calculating the Minimal Spanning Tree of a graph operating on metric objects, and show that it reduces the number of distance computations needed.

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Benhaddou, Rida. "Nonparametric and Empirical Bayes Estimation Methods." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5765.

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In the present dissertation, we investigate two different nonparametric models; empirical Bayes model and functional deconvolution model. In the case of the nonparametric empirical Bayes estimation, we carried out a complete minimax study. In particular, we derive minimax lower bounds for the risk of the nonparametric empirical Bayes estimator for a general conditional distribution. This result has never been obtained previously. In order to attain optimal convergence rates, we use a wavelet series based empirical Bayes estimator constructed in Pensky and Alotaibi (2005). We propose an adaptive version of this estimator using Lepski's method and show that the estimator attains optimal convergence rates. The theory is supplemented by numerous examples. Our study of the functional deconvolution model expands results of Pensky and Sapatinas (2009, 2010, 2011) to the case of estimating an (r+1)-dimensional function or dependent errors. In both cases, we derive minimax lower bounds for the integrated square risk over a wide set of Besov balls and construct adaptive wavelet estimators that attain those optimal convergence rates. In particular, in the case of estimating a periodic (r+1)-dimensional function, we show that by choosing Besov balls of mixed smoothness, we can avoid the ''curse of dimensionality'' and, hence, obtain higher than usual convergence rates when r is large. The study of deconvolution of a multivariate function is motivated by seismic inversion which can be reduced to solution of noisy two-dimensional convolution equations that allow to draw inference on underground layer structures along the chosen profiles. The common practice in seismology is to recover layer structures separately for each profile and then to combine the derived estimates into a two-dimensional function. By studying the two-dimensional version of the model, we demonstrate that this strategy usually leads to estimators which are less accurate than the ones obtained as two-dimensional functional deconvolutions. Finally, we consider a multichannel deconvolution model with long-range dependent Gaussian errors. We do not limit our consideration to a specific type of long-range dependence, rather we assume that the eigenvalues of the covariance matrix of the errors are bounded above and below. We show that convergence rates of the estimators depend on a balance between the smoothness parameters of the response function, the smoothness of the blurring function, the long memory parameters of the errors, and how the total number of observations is distributed among the channels.
Ph.D.
Doctorate
Mathematics
Sciences
Mathematics

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Reinhardt, Timothy Patrick. "Empirical methods for comparing governance structure." Thesis, [Austin, Tex. : University of Texas, 2009. http://hdl.handle.net/2152/ETD-UT-2009-05-134.

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Mikkola, Hennamari. "Empirical studies on Finnish hospital pricing methods /." Helsinki : Helsinki School of Economics, 2002. http://aleph.unisg.ch/hsgscan/hm00068878.pdf.

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Brandel, John. "Empirical Bayes methods for missing data analysis." Thesis, Uppsala University, Department of Mathematics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121408.

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Lönnstedt, Ingrid. "Empirical Bayes Methods for DNA Microarray Data." Doctoral thesis, Uppsala University, Department of Mathematics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5865.

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cDNA microarrays is one of the first high-throughput gene expression technologies that has emerged within molecular biology for the purpose of functional genomics. cDNA microarrays compare the gene expression levels between cell samples, for thousands of genes simultaneously.

The microarray technology offers new challenges when it comes to data analysis, since the thousands of genes are examined in parallel, but with very few replicates, yielding noisy estimation of gene effects and variances. Although careful image analyses and normalisation of the data is applied, traditional methods for inference like the Student t or Fisher’s F-statistic fail to work.

In this thesis, four papers on the topics of empirical Bayes and full Bayesian methods for two-channel microarray data (as e.g. cDNA) are presented. These contribute to proving that empirical Bayes methods are useful to overcome the specific data problems. The sample distributions of all the genes involved in a microarray experiment are summarized into prior distributions and improves the inference of each single gene.

The first part of the thesis includes biological and statistical background of cDNA microarrays, with an overview of the different steps of two-channel microarray analysis, including experimental design, image analysis, normalisation, cluster analysis, discrimination and hypothesis testing. The second part of the thesis consists of the four papers. Paper I presents the empirical Bayes statistic B, which corresponds to a t-statistic. Paper II is based on a version of B that is extended for linear model effects. Paper III assesses the performance of empirical Bayes models by comparisons with full Bayes methods. Paper IV provides extensions of B to what corresponds to F-statistics.

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Lönnstedt, Ingrid. "Empirical Bayes methods for DNA microarray data /." Uppsala : Matematiska institutionen, Univ. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5865.

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Imhof, David. "Empirical Methods for Detecting Bid-rigging Cartels." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCB005/document.

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Le projet de thèse présente différentes méthodes empiriques permettant de détecter des cartels. Il vise à démontrer premièrement que des résultats efficaces peuvent être obtenus avec de simples indicateurs statistiques et deuxièmement que les méthodes économétriques traditionnelles ne sont pas aussi efficaces
The PhD studies different empirical methods to detect bid-rigging cartels. It shows first that simple statistical screens perform very well to detect bid-rigging infringement. Second, the econometric method of Bajari, well established in the literature, produces poor results

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Книги з теми "Empirical methods"

Maritz, J. S. Empirical Bayes methods. 2nd ed. London: Chapman and Hall, 1989.

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K, Robbennolt Jennifer, and Ulen Thomas, eds. Empirical methods in law. New York: Aspen Publishers, 2010.

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Lawless, Robert M. Empirical methods in law. New York: Aspen Publishers, 2010.

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Cohen, Paul R. Empirical methods for artificial intelligence. Cambridge, Ma: MIT Press, 1995.

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Empirical methods for artificial intelligence. Cambridge, Mass: MIT Press, 1995.

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Sudhoff, Stefan, Denisa Lenertova, Roland Meyer, Sandra Pappert, Petra Augurzky, Ina Mleinek, Nicole Richter, and Johannes Schließer, eds. Methods in Empirical Prosody Research. Berlin, Boston: DE GRUYTER, 2006. http://dx.doi.org/10.1515/9783110914641.

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Bailey, Michael P. Empirical methods for estimating workload capacity. Monterey, Calif: Naval Postgraduate School, 1992.

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1962-, Christensen H. I., and Phillips P. Jonathon, eds. Empirical evaluation methods in computer vision. Singapore: World Scientific, 2002.

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Willi, Semmler, ed. Business cycles: Theory and empirical methods. Boston: Kluwer Academic Publishers, 1994.

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Semi-empirical methods of quantum chemistry. Chichester: Ellis Horwood, 1985.

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Частини книг з теми "Empirical methods"

Volponi, Allan J. "Empirical Methods." In Gas Turbine Parameter Corrections, 61–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41076-6_7.

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O’Connell, Daniel C., and Sabine Kowal. "Empirical Methods." In Communicating with One Another, 1–14. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77632-3_2.

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Hult, Henrik, Filip Lindskog, Ola Hammarlid, and Carl Johan Rehn. "Empirical Methods." In Risk and Portfolio Analysis, 197–229. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4103-8_7.

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Häder, Michael. "Survey Methods." In Empirical Social Research, 173–311. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37907-0_6.

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Patten, Mildred L., and Michelle Newhart. "Empirical Research." In Understanding Research Methods, 5–7. Tenth edition. | New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315213033-3.

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Patten, Mildred L., and Michelle Newhart. "Empirical Validity." In Understanding Research Methods, 129–32. Tenth edition. | New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315213033-42.

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Wu, Changbao, and Mary E. Thompson. "Empirical Likelihood Methods." In ICSA Book Series in Statistics, 161–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44246-0_8.

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Cai, Kai-Yuan. "Empirical Regression Methods." In The Kluwer International Series in Software Engineering, 29–68. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5593-3_2.

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Knopov, Pavel S., and Evgeniya J. Kasitskaya. "Parametric Empirical Methods." In Applied Optimization, 11–70. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3567-3_2.

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Szutowski, Dawid. "Empirical Research Methods." In Management Control Systems, Decision-Making, and Innovation Development, 217–56. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003215035-6.

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Тези доповідей конференцій з теми "Empirical methods"

Grendar, M. "Empirical Maximum Entropy Methods." In Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423302.

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Pakalnis, Rimas. "Empirical design methods in practice." In International Seminar on Design Methods in Underground Mining. Australian Centre for Geomechanics, Perth, 2015. http://dx.doi.org/10.36487/acg_rep/1511_0.3_pakalnis.

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Tadepalli, Srikanth, and Kristin L. Wood. "Adaptive Methods for Non-Linear Empirical Similitude Method." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67974.

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Анотація:

Several problems in engineering realm pose modeling and simulation difficulty due to severe non–linear behavior and debilitating singular or stiff conditions that act as additional impediments. In many such instances advanced numerical schemes are employed to either relax or simplify the PDE that defines the physical process to obtain reasonable output from the simulation. Digressing from this traditional approach, we present an experimental similitude method in this paper to analyze non–linear systems. Combined with the use of original mapping algorithms, we discuss the benefits of empirical similarity techniques and present a heat transfer example for exposition.

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Koehn, Philipp, and Kevin Knight. "Empirical methods for compound splitting." In the tenth conference. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1067807.1067833.

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Tsumoto, Shusaku. "Empirical Rule Induction Methods Selection." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671726.

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Paek, Tim. "Empirical methods for evaluating dialog systems." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1118053.1118054.

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Paek, Tim. "Empirical methods for evaluating dialog systems." In the Second SIGdial Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1118078.1118092.

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Alnatheer, Ahmed, Andrew M. Gravell, David Argles, and Lester Gilbert. "Agile Security Methods: An Empirical Investigation." In Software Engineering / 811: Parallel and Distributed Computing and Networks / 816: Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.810-011.

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Wood, John J., Kristin L. Wood, and Wade O. Troxell. "Empirical Analysis Using Advanced Similarity Methods." 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-34082.

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Анотація:

Traditional dimensional analysis techniques for predicting the performance characteristics of a product can be greatly improved in both accuracy and domain of applicability by the infusion of empirical data, derived from material tests, into the equations that characterize the system parameters of interest. Advanced similarity methods are investigated which overcome the constraints associated with the traditional methods and provide increased analysis capability and improved insight into the phenomenon governing the problem. Such capability greatly increases the design toolbox available to product developers, across a large range of scale and application. It also significantly enhances a developer’s choices for prototype portioning during a development cycle. Solid mechanics and heat transfer applications are used to illustrate the basic utility of the methods.

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Sharma, Shelja, Veena Mittal, Ritesh Srivastava, and S. K. Singh. "Empirical Evaluation of Various Classification Methods." In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2020. http://dx.doi.org/10.1109/icacccn51052.2020.9362773.

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Звіти організацій з теми "Empirical methods"

Glaeser, Edward. Researcher Incentives and Empirical Methods. Cambridge, MA: National Bureau of Economic Research, October 2006. http://dx.doi.org/10.3386/t0329.

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Birenzvige, A., L. M. Sturdivan, G. R. Famini, P. N. Krishnan, and R. E. Morris. Predicting Polymer Properties by Computational Methods 2: A Comparison of Semi-Empirical Methods. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada256856.

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Cain, P. Empirical methods of support design for the Sydney Coalfield. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1985. http://dx.doi.org/10.4095/304789.

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Gu, Jiaying, and Roger Koenker. Rebayes: an R package for empirical bayes mixture methods. The IFS, August 2017. http://dx.doi.org/10.1920/wp.cem.2017.3717.

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Rosati, Julie D. Functional Design of Breakwaters for Shore Protection: Empirical Methods. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada228024.

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Tetenov, Aleksey, and Toru Kitagawa. Who should be treated? Empirical welfare maximization methods for treatment choice. IFS, March 2015. http://dx.doi.org/10.1920/wp.cem.2015.1015.

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Kitagawa, Toru, and Aleksey Tetenov. Who should be treated? Empirical welfare maximization methods for treatment choice. The IFS, May 2017. http://dx.doi.org/10.1920/wp.cem.2017.2417.

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Kunsberg, P. Citizen advisory boards: An empirical model for choosing goals and methods. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/543603.

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Malde, Bansi, and Arun Advani. Empirical methods for networks data: social effects, network formation and measurement error. IFS, December 2014. http://dx.doi.org/10.1920/wp.ifs.2014.1434.

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Jagannathan, Ravi, and Zhenyu Wang. Empirical Evaluation of Asset Pricing Models: A Comparison of the SDF and Beta Methods. Cambridge, MA: National Bureau of Economic Research, January 2001. http://dx.doi.org/10.3386/w8098.

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