Добірка наукової літератури з теми "Principal components"
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Статті в журналах з теми "Principal components"
Sutter, Jon M., John H. Kalivas, and Patrick M. Lang. "Which principal components to utilize for principal component regression." Journal of Chemometrics 6, no. 4 (July 1992): 217–25. http://dx.doi.org/10.1002/cem.1180060406.
Повний текст джерелаAbegaz, Fentaw, Kridsadakorn Chaichoompu, Emmanuelle Génin, David W. Fardo, Inke R. König, Jestinah M. Mahachie John, and Kristel Van Steen. "Principals about principal components in statistical genetics." Briefings in Bioinformatics 20, no. 6 (September 14, 2018): 2200–2216. http://dx.doi.org/10.1093/bib/bby081.
Повний текст джерелаHaswell, S. J. "Principal Components." Analytica Chimica Acta 309, no. 1-3 (June 1995): 405–6. http://dx.doi.org/10.1016/0003-2670(95)90335-6.
Повний текст джерелаMiyazaki, Haruo, and Youichi Seki. "Principal Components and Principal Clusters." Journal of Information and Optimization Sciences 8, no. 2 (May 1987): 189–99. http://dx.doi.org/10.1080/02522667.1987.10698885.
Повний текст джерелаFujiwara, Masakazu, Tomohiro Minamidani, Isamu Nagai, and Hirofumi Wakaki. "Principal Components Regression by Using Generalized Principal Components Analysis." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 43, no. 1 (2013): 57–78. http://dx.doi.org/10.14490/jjss.43.57.
Повний текст джерелаSaegusa, Ryo, Hitoshi Sakano, and Shuji Hashimoto. "Nonlinear principal component analysis to preserve the order of principal components." Neurocomputing 61 (October 2004): 57–70. http://dx.doi.org/10.1016/j.neucom.2004.03.004.
Повний текст джерелаMertens, B. J. A., T. Fearn, and M. Thompson. "Efficient cross-validation of principal components applied to principal component regression." Statistics and Computing 6, no. 2 (June 1996): 178. http://dx.doi.org/10.1007/bf00162530.
Повний текст джерелаWhitlark, David, and George H. Dunteman. "Principal Components Analysis." Journal of Marketing Research 27, no. 2 (May 1990): 243. http://dx.doi.org/10.2307/3172855.
Повний текст джерелаAmmann, Larry P. "Robust Principal Components." Communications in Statistics - Simulation and Computation 18, no. 3 (January 1989): 857–74. http://dx.doi.org/10.1080/03610918908812795.
Повний текст джерелаFranses, Philip Hans, and Eva Janssens. "Spurious principal components." Applied Economics Letters 26, no. 1 (February 2018): 37–39. http://dx.doi.org/10.1080/13504851.2018.1433292.
Повний текст джерелаДисертації з теми "Principal components"
Nunes, Madalena Baioa Paraíso. "Portfolio selection : a study using principal component analysis." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14598.
Повний текст джерелаNesta tese aplicámos a análise de componentes principais ao mercado bolsista português usando os constituintes do índice PSI-20, de Julho de 2008 a Dezembro de 2016. Os sete primeiros componentes principais foram retidos, por se ter verificado que estes representavam as maiores fontes de risco deste mercado em específico. Assim, foram construídos sete portfólios principais e comparámo-los com outras estratégias de alocação. Foram construídos o portfólio 1/N (portfólio com investimento igual para cada um dos 26 ativos), o PPEqual (portfólio com igual investimento em cada um dos 7 principal portfólios) e o portfólio MV (portfólio que tem por base a teoria moderna de gestão de carteiras de Markowitz (1952)). Concluímos que estes dois últimos portfólios apresentavam os melhores resultados em termos de risco e retorno, sendo o portfólio PPEqual mais adequado a um investidor com maior grau de aversão ao risco e o portfólio MV mais adequado a um investidor que estaria disposto a arriscar mais em prol de maior retorno. No que diz respeito ao nível de risco, o PPEqual é o portfólio com melhores resultados e nenhum outro portfólio conseguiu apresentar valores semelhantes. Assim encontrámos um portfólio que é a ponderação de todos os portfólios principais por nós construídos e este era o portfólio mais eficiente em termos de risco.
In this thesis we apply principal component analysis to the Portuguese stock market using the constituents of the PSI-20 index from July 2008 to December 2016. The first seven principal components were retained, as we verified that these represented the major risk sources in this specific market. Seven principal portfolios were constructed and we compared them with other allocation strategies. The 1/N portfolio (with an equal investment in each of the 26 stocks), the PPEqual portfolio (with an equal investment in each of the 7 principal portfolios) and the MV portfolio (based on Markowitz's (1952) mean-variance strategy) were constructed. We concluded that these last two portfolios presented the best results in terms of return and risk, with PPEqual portfolio being more suitable for an investor with a greater degree of risk aversion and the MV portfolio more suitable for an investor willing to risk more in favour of higher returns. Regarding the level of risk, PPEqual is the portfolio with the best results and, so far, no other portfolio has presented similar values. Therefore, we found an equally-weighted portfolio among all the principal portfolios we built, which was the most risk efficient.
info:eu-repo/semantics/publishedVersion
Kassaye, Meseret Haile, and Yigit Demir. "Calibration Based On Principal Components." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-26582.
Повний текст джерелаSun, Linjuan. "Simple principal components." Thesis, Open University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429528.
Повний текст джерелаDuras, Toni. "Aspects of common principal components." Licentiate thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-38587.
Повний текст джерелаBrubaker, S. Charles. "Extensions of principal components analysis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29645.
Повний текст джерелаCommittee Chair: Santosh Vempala; Committee Member: Adam Kalai; Committee Member: Haesun Park; Committee Member: Ravi Kannan; Committee Member: Vladimir Koltchinskii. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Khwambala, Patricia Helen. "The importance of selecting the optimal number of principal components for fault detection using principal component analysis." Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/11930.
Повний текст джерелаIncludes bibliographical references.
Fault detection and isolation are the two fundamental building blocks of process monitoring. Accurate and efficient process monitoring increases plant availability and utilization. Principal component analysis is one of the statistical techniques that are used for fault detection. Determination of the number of PCs to be retained plays a big role in detecting a fault using the PCA technique. In this dissertation focus has been drawn on the methods of determining the number of PCs to be retained for accurate and effective fault detection in a laboratory thermal system. SNR method of determining number of PCs, which is a relatively recent method, has been compared to two commonly used methods for the same, the CPV and the scree test methods.
Zavala, Frank Alcorta. "Principals' Perceptions of the Most Important Components in an Effective Principal Preparation Program." ScholarWorks, 2014. https://scholarworks.waldenu.edu/dissertations/26.
Повний текст джерелаBrandenberg, Romano Rodolfo. "Principal Components Analysis of Commodity Trading Advisors." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/02604577002/$FILE/02604577002.pdf.
Повний текст джерелаUddin, Mudassir. "Interpretation of results from simplified principal components." Thesis, University of Aberdeen, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301216.
Повний текст джерелаNeuenschwander, Beat. "Common principal components for dependent random vectors /." [Bern] : [s.n.], 1991. http://www.ub.unibe.ch/content/bibliotheken_sammlungen/sondersammlungen/dissen_bestellformular/index_ger.html.
Повний текст джерелаКниги з теми "Principal components"
Principal components analysis. Newbury Park, Calif: Sage, 1989.
Знайти повний текст джерелаDunteman, George. Principal Components Analysis. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1989. http://dx.doi.org/10.4135/9781412985475.
Повний текст джерелаDunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.
Знайти повний текст джерелаDunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.
Знайти повний текст джерелаPrincipal component analysis. New York: Springer-Verlag, 1986.
Знайти повний текст джерелаJolliffe, I. T. Principal component analysis. 2nd ed. New York: Springer, 2010.
Знайти повний текст джерелаPrincipal component analysis. 2nd ed. New York: Springer, 2002.
Знайти повний текст джерелаA user's guide to principal components. Hoboken, N.J: Wiley-Interscience, 2003.
Знайти повний текст джерелаLeBlanc, Michael R. Adaptive principal surfaces. Toronto: University of Toronto, Dept. of Statistics, 1991.
Знайти повний текст джерелаJackson, J. Edward. A user's guide to principal components. New York: Wiley, 1991.
Знайти повний текст джерелаЧастини книг з теми "Principal components"
Kloek, T. "Principal Components." In Time Series and Statistics, 204–7. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_27.
Повний текст джерелаFalk, Michael, Frank Marohn, and Bernward Tewes. "Principal Components." In Foundations of Statistical Analyses and Applications with SAS, 321–61. Basel: Birkhäuser Basel, 1995. http://dx.doi.org/10.1007/978-3-0348-8195-1_8.
Повний текст джерелаBrown, Charles E. "Principal Components." In Applied Multivariate Statistics in Geohydrology and Related Sciences, 103–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-80328-4_9.
Повний текст джерелаKloek, T. "Principal Components." In The New Palgrave Dictionary of Economics, 1–4. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_1776-1.
Повний текст джерелаKloek, T. "Principal Components." In The New Palgrave Dictionary of Economics, 10747–49. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_1776.
Повний текст джерелаHatanaka, Michio, and Hiroshi Yamada. "Principal Components." In Co-trending: A Statistical System Analysis of Economic Trends, 25–27. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-65912-9_4.
Повний текст джерелаJolicoeur, Pierre. "Principal components or principal axes." In Introduction to Biometry, 280–302. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4777-8_32.
Повний текст джерелаHilbert, Sven, and Markus Bühner. "Principal Components Analysis." In Encyclopedia of Personality and Individual Differences, 4030–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1340.
Повний текст джерелаHärdle, Wolfgang, and Léopold Simar. "Principal Components Analysis." In Applied Multivariate Statistical Analysis, 233–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_9.
Повний текст джерелаEveritt, Brian S., and Graham Dunn. "Principal Components Analysis." In Applied Multivariate Data Analysis, 48–73. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch3.
Повний текст джерелаТези доповідей конференцій з теми "Principal components"
SAUCIER, ANTOINE. "LOCALIZED PRINCIPAL COMPONENTS." In Conference on Fractals 2002. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777720_0028.
Повний текст джерелаSAUCIER, ANTOINE. "MULTISCALE PRINCIPAL COMPONENTS." In Fractals and Related Phenomena in Nature. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702746_0024.
Повний текст джерелаBishop, C. M. "Variational principal components." In 9th International Conference on Artificial Neural Networks: ICANN '99. IEE, 1999. http://dx.doi.org/10.1049/cp:19991160.
Повний текст джерелаGuo, Hao, Kurt J. Marfurt, Jianlei Liu, and Qifeng Dou. "Principal components analysis of spectral components." In SEG Technical Program Expanded Abstracts 2006. Society of Exploration Geophysicists, 2006. http://dx.doi.org/10.1190/1.2370422.
Повний текст джерелаAquilina, Kirsty, David A. W. Barton, and Nathan F. Lepora. "Principal Components of Touch." In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8461045.
Повний текст джерелаBoutsidis, Christos, Dan Garber, Zohar Karnin, and Edo Liberty. "Online Principal Components Analysis." In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973730.61.
Повний текст джерелаFoi, Alessandro, and Giacomo Boracchi. "Nonlocal foveated principal components." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884596.
Повний текст джерелаFirmansyah, Gerry, Zainal A. Hasibuan, and Yudho Giri Sucahyo. "Indonesia e-Government components: A principal component analysis approach." In 2014 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 2014. http://dx.doi.org/10.1109/icitsi.2014.7048255.
Повний текст джерелаWei-min, Liu, and Chang Chein-I. "Variants of Principal Components Analysis." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4422989.
Повний текст джерелаHoang, A. "Information retrieval with principal components." In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. IEEE, 2004. http://dx.doi.org/10.1109/itcc.2004.1286463.
Повний текст джерелаЗвіти організацій з теми "Principal components"
Chen, Xiaohong, Jose A. Scheinkman, and Lars Peter Hansen. Principal components and the long run. Institute for Fiscal Studies, May 2009. http://dx.doi.org/10.1920/wp.cem.2009.0709.
Повний текст джерелаNeumann, Michael, and Hans Schneider. Principal Components of Minus M-Matrices. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada232354.
Повний текст джерелаLiu, Yong, and Harel Shouval. Principal Components of Natural Images: An Analytical Solution. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada264800.
Повний текст джерелаFekedulegn, B. Desta, J. J. Colbert, R. R. ,. Jr Hicks, and Michael E. Schuckers. Coping with Multicollinearity: An Example on Application of Principal Components Regression in Dendroecology. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station, 2002. http://dx.doi.org/10.2737/ne-rp-721.
Повний текст джерелаHarter, Rachel M., Pinliang (Patrick) Chen, Joseph P. McMichael, Edgardo S. Cureg, Samson A. Adeshiyan, and Katherine B. Morton. Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey. RTI Press, May 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0041.1705.
Повний текст джерелаMARTIN, SHAWN B. Kernel Near Principal Component Analysis. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/810934.
Повний текст джерелаSchennach, Susanne M., and Florian Gunsilius. A nonlinear principal component decomposition. The IFS, March 2017. http://dx.doi.org/10.1920/wp.cem.2017.1617.
Повний текст джерелаFlicker, Dawn, James Carney, Mary Cusentino, Khalid Hattar, Michael Steinkamp, and Larico Treadwell. Assessment of Sandia?s 2021 Pilot Program for Research Traineeships to Broaden and Diversify Fusion Energy Science: Development and Rapid Screening of Refractory Multi-Principal Elemental Composites for Plasma Facing Components. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1855066.
Повний текст джерелаAït-Sahalia, Yacine, and Dacheng Xiu. Principal Component Analysis of High Frequency Data. Cambridge, MA: National Bureau of Economic Research, September 2015. http://dx.doi.org/10.3386/w21584.
Повний текст джерелаDing, Chris H. Q., Xiaofeng He, Hongyuan Zha, and Horst D. Simon. Self-aggregation in scaled principal component space. Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/820779.
Повний текст джерела