Auswahl der wissenschaftlichen Literatur zum Thema „Empirical methods“
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Zeitschriftenartikel zum Thema "Empirical methods":
Lindley, D. V., J. S. Maritz und T. Lwin. „Empirical Bayes Methods“. Mathematical Gazette 74, Nr. 467 (März 1990): 91. http://dx.doi.org/10.2307/3618894.
Bagghi, Parthasarathy, J. S. Maritz und T. Lwin. „Empirical Bayes Methods.“ Journal of the American Statistical Association 86, Nr. 413 (März 1991): 244. http://dx.doi.org/10.2307/2289739.
Angus, John E. „Empirical Bayes Methods“. Technometrics 33, Nr. 2 (Mai 1991): 243–45. http://dx.doi.org/10.1080/00401706.1991.10484821.
Stephenson, W. Robert. „Empirical Bayes Methods“. Journal of Quality Technology 22, Nr. 3 (Juli 1990): 249–50. http://dx.doi.org/10.1080/00224065.1990.11979250.
Young, Karen, J. Maritz und T. Lwin. „Empirical Bayes Methods.“ Applied Statistics 41, Nr. 3 (1992): 604. http://dx.doi.org/10.2307/2348097.
Denham, Mike, J. S. Maritz und T. Lwin. „Empirical Bayes Methods.“ Statistician 39, Nr. 1 (1990): 97. http://dx.doi.org/10.2307/2348214.
Schneider-Mayerson, Matthew, Alexa Weik von Mossner und W. P. Małecki. „Empirical Ecocriticism: Environmental Texts and Empirical Methods“. ISLE: Interdisciplinary Studies in Literature and Environment 27, Nr. 2 (2020): 327–36. http://dx.doi.org/10.1093/isle/isaa022.
ter Beek, Maurice H., und Alessio Ferrari. „Empirical Formal Methods: Guidelines for Performing Empirical Studies on Formal Methods“. Software 1, Nr. 4 (24.09.2022): 381–416. http://dx.doi.org/10.3390/software1040017.
Wolstein, Benjamin. „Five Empirical Psychoanalytic Methods“. Contemporary Psychoanalysis 26, Nr. 2 (April 1990): 237–56. http://dx.doi.org/10.1080/00107530.1990.10746657.
Laird, Nan M., und Thomas A. Louis. „Empirical Bayes Ranking Methods“. Journal of Educational Statistics 14, Nr. 1 (März 1989): 29–46. http://dx.doi.org/10.3102/10769986014001029.
Dissertationen zum Thema "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.
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.
Zawadzki, Erik P. „Multiagent learning and empirical methods“. Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2480.
Fevang, Rune, und 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.
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.
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.
Ph.D.
Doctorate
Mathematics
Sciences
Mathematics
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.
Mikkola, Hennamari. „Empirical studies on Finnish hospital pricing methods /“. Helsinki : Helsinki School of Economics, 2002. http://aleph.unisg.ch/hsgscan/hm00068878.pdf.
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.
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.
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.
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.
Imhof, David. „Empirical Methods for Detecting Bid-rigging Cartels“. Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCB005/document.
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
Bücher zum Thema "Empirical methods":
Maritz, J. S. Empirical Bayes methods. 2. Aufl. London: Chapman and Hall, 1989.
Lawless, Robert M. Empirical methods in law. New York: Aspen Publishers, 2010.
Lawless, Robert M. Empirical methods in law. New York: Aspen Publishers, 2010.
Cohen, Paul R. Empirical methods for artificial intelligence. Cambridge, Ma: MIT Press, 1995.
Cohen, Paul R. Empirical methods for artificial intelligence. Cambridge, Mass: MIT Press, 1995.
Sudhoff, Stefan, Denisa Lenertova, Roland Meyer, Sandra Pappert, Petra Augurzky, Ina Mleinek, Nicole Richter und Johannes Schließer, Hrsg. Methods in Empirical Prosody Research. Berlin, Boston: DE GRUYTER, 2006. http://dx.doi.org/10.1515/9783110914641.
Bailey, Michael P. Empirical methods for estimating workload capacity. Monterey, Calif: Naval Postgraduate School, 1992.
1962-, Christensen H. I., und Phillips P. Jonathon, Hrsg. Empirical evaluation methods in computer vision. Singapore: World Scientific, 2002.
Willi, Semmler, Hrsg. Business cycles: Theory and empirical methods. Boston: Kluwer Academic Publishers, 1994.
Sadlej, Joanna. Semi-empirical methods of quantum chemistry. Chichester: Ellis Horwood, 1985.
Buchteile zum Thema "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.
O’Connell, Daniel C., und 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.
Hult, Henrik, Filip Lindskog, Ola Hammarlid und 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.
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.
Patten, Mildred L., und 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.
Patten, Mildred L., und 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.
Wu, Changbao, und 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.
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.
Knopov, Pavel S., und 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.
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.
Konferenzberichte zum Thema "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.
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.
Tadepalli, Srikanth, und 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.
Koehn, Philipp, und 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.
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.
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.
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.
Alnatheer, Ahmed, Andrew M. Gravell, David Argles und 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.
Wood, John J., Kristin L. Wood und 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.
Sharma, Shelja, Veena Mittal, Ritesh Srivastava und 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.
Berichte der Organisationen zum Thema "Empirical methods":
Glaeser, Edward. Researcher Incentives and Empirical Methods. Cambridge, MA: National Bureau of Economic Research, Oktober 2006. http://dx.doi.org/10.3386/t0329.
Birenzvige, A., L. M. Sturdivan, G. R. Famini, P. N. Krishnan und 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.
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.
Gu, Jiaying, und 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.
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.
Tetenov, Aleksey, und Toru Kitagawa. Who should be treated? Empirical welfare maximization methods for treatment choice. IFS, März 2015. http://dx.doi.org/10.1920/wp.cem.2015.1015.
Kitagawa, Toru, und Aleksey Tetenov. Who should be treated? Empirical welfare maximization methods for treatment choice. The IFS, Mai 2017. http://dx.doi.org/10.1920/wp.cem.2017.2417.
Kunsberg, P. Citizen advisory boards: An empirical model for choosing goals and methods. Office of Scientific and Technical Information (OSTI), Dezember 1994. http://dx.doi.org/10.2172/543603.
Malde, Bansi, und Arun Advani. Empirical methods for networks data: social effects, network formation and measurement error. IFS, Dezember 2014. http://dx.doi.org/10.1920/wp.ifs.2014.1434.
Jagannathan, Ravi, und Zhenyu Wang. Empirical Evaluation of Asset Pricing Models: A Comparison of the SDF and Beta Methods. Cambridge, MA: National Bureau of Economic Research, Januar 2001. http://dx.doi.org/10.3386/w8098.