Academic literature on the topic 'Principal components analysis'
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Journal articles on the topic "Principal components analysis"
Whitlark, 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.
Full textKim, Sung-Hoon, and George H. Dunteman. "Principal Components Analysis." Journal of Educational Statistics 16, no. 2 (1991): 141. http://dx.doi.org/10.2307/1165117.
Full textFujiwara, 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.
Full textAbegaz, 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.
Full textVoegtlin, Thomas. "Recursive principal components analysis." Neural Networks 18, no. 8 (October 2005): 1051–63. http://dx.doi.org/10.1016/j.neunet.2005.07.005.
Full textMaćkiewicz, Andrzej, and Waldemar Ratajczak. "Principal components analysis (PCA)." Computers & Geosciences 19, no. 3 (March 1993): 303–42. http://dx.doi.org/10.1016/0098-3004(93)90090-r.
Full textYendle, Peter W., and Halliday J. H. MacFie. "Discriminant principal components analysis." Journal of Chemometrics 3, no. 4 (September 1989): 589–600. http://dx.doi.org/10.1002/cem.1180030407.
Full textSaegusa, 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.
Full textRutledge, Douglas N. "Comparison of Principal Components Analysis, Independent Components Analysis and Common Components Analysis." Journal of Analysis and Testing 2, no. 3 (July 2018): 235–48. http://dx.doi.org/10.1007/s41664-018-0065-5.
Full textBoudou, Alain, and Sylvie Viguier-Pla. "Principal components analysis and cyclostationarity." Journal of Multivariate Analysis 189 (May 2022): 104875. http://dx.doi.org/10.1016/j.jmva.2021.104875.
Full textDissertations / Theses on the topic "Principal components analysis"
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.
Full textNesta 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
Brubaker, S. Charles. "Extensions of principal components analysis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29645.
Full textCommittee 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.
Brandenberg, 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.
Full textFučík, Vojtěch. "Principal component analysis in Finance." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.
Full textKhwambala, 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.
Full textIncludes 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.
Brennan, Victor L. "Principal component analysis with multiresolution." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7079/brennan%5Fdissertation.pdf.
Full textTitle from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).
López, Alfageme Alfredo Ignacio. "Nonlinear principal components analysis for measures and images." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/114861.
Full textEn esta tesis definimos dos adaptaciones no-lineales del análisis de componentes principales, para el estudio de la variabilidad de datos conformados por medidas de probabilidad y por imágenes. En el Capitulo 2 introducimos el método de análisis de componentes principales geodésico (ACPG) en el espacio de medidas de probabilidad en la línea real, con segundo momento finito, dotado de la métrica de Wasserstein. Apoyándonos en la estructura pseudo-riemanniana del espacio de Wasserstein, definimos el ACPG basado en adaptaciones del ACP a variedades, propuestas en la literatura. En este contexto, el ACPG se define por medio de un problema de minimización sobre el espacio conformado por los subconjuntos geodésicos del espacio de Wasserstein. Usando argumentos de compacidad y de gama-convergencia, establecemos la consistencia del método, demostrando que el ACPG converge a su contraparte poblacional, cuando el tamaño de la muestra crece a infinito. Discutimos las ventajas de este método, respecto a un ACP funcional estándar de medidas de probabilidad en el espacio de Hilbert de funciones a cuadrado integrable. Con el fin de mostrar los beneficios de este procedimiento para el análisis de datos, exhibimos algunos ejemplos ilustrativos en un modelo estadístico simple. En el Capitulo 3 describimos el método de análisis de componentes principales geométrico (ACP geométrico) para analizar los modos principales de variación geométrica de un conjunto de imágenes. En este contexto proponemos modelar la variabilidad geométrica de las imágenes, respecto a un patrón medio de referencia, por medio de un operador de deformación parametrizado por un espacio de Hilbert. El ACP geométrico consta de dos etapas: (1) registro de imágenes usando un operador de deformación y (2) ACP estándar en los parámetros asociados a las deformaciones. La consistencia del procedimiento es analizada en el contexto de un modelo estadístico de patrón deformable, con una doble asíntota, donde el número de observaciones tiende a infinito y el ruido aditivo converge a cero. Para destacar los beneficios de este procedimiento, describimos un algoritmo y su aplicación a algunos experimentos numéricos con imágenes reales.
Bhamani, Feroz. "Hedging Interest-Rate Options Using Principal Components Analysis." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29250.
Full textPatak, Zdenek. "Robust principal component analysis via projection pursuit." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29737.
Full textScience, Faculty of
Statistics, Department of
Graduate
Wedlake, Ryan Stuart. "Robust principal component analysis biplots." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/929.
Full textBooks on the topic "Principal components analysis"
Dunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.
Find full textDunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.
Find full textDunteman, 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.
Full textJolliffe, I. T. Principal component analysis. 2nd ed. New York: Springer, 2010.
Find full textHyvarinen, Aapo. Independent component analysis. New York: J. Wiley, 2001.
Find full textJuha, Karhunen, and Oja Erkki, eds. Independent component analysis. New York: J. Wiley, 2001.
Find full textJackson, J. Edward. A user's guide to principal components. New York: Wiley, 1991.
Find full textFlury, Bernhard. Common principal components and related multivariate models. New York: Wiley, 1988.
Find full textLeBlanc, Michael R. Adaptive principal surfaces. Toronto: University of Toronto, Dept. of Statistics, 1991.
Find full textJ, Dunn W., Scott D. R. 1934-, and United States. Environmental Protection Agency., eds. Principal components analysis and partial least squares regression. [Washington, D.C.?: U.S. Environmental Protection Agency, 1992.
Find full textBook chapters on the topic "Principal components analysis"
Murtagh, Fionn, and André Heck. "Principal Components Analysis." In Multivariate Data Analysis, 13–53. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_2.
Full textHilbert, 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.
Full textHärdle, 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.
Full textEveritt, 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.
Full textHärdle, Wolfgang Karl, and Léopold Simar. "Principal Components Analysis." In Applied Multivariate Statistical Analysis, 319–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_11.
Full textEveritt, Brian Sidney. "Principal Components Analysis." In Springer Texts in Statistics, 41–64. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-124-5_3.
Full textHärdle, Wolfgang Karl, and Léopold Simar. "Principal Components Analysis." In Applied Multivariate Statistical Analysis, 269–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_10.
Full textAtkinson, Anthony C., Marco Riani, and Andrea Cerioli. "Principal Components Analysis." In Springer Series in Statistics, 229–96. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-21840-3_5.
Full textAfifi, A. A., and V. Clark. "Principal components analysis." In Computer-Aided Multivariate Analysis, 330–53. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-3342-3_14.
Full textFrieden, B. Roy. "Principal Components Analysis." In Probability, Statistical Optics, and Data Testing, 363–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56699-8_15.
Full textConference papers on the topic "Principal components analysis"
Guo, 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.
Full textBoutsidis, 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.
Full textWei-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.
Full textFirmansyah, 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.
Full textSteinherz, T., N. Intrator, and E. Rivlin. "Skew detection via principal components analysis." In Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318). IEEE, 1999. http://dx.doi.org/10.1109/icdar.1999.791747.
Full textGhisu, Tiziano, Geoffrey Parks, Jerome Jarrett, and P. Clarkson. "Accelerating Design Optimization Via Principal Components Analysis." In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-5855.
Full textAllen, Genevera I. "Multi-way functional principal components analysis." In 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714047.
Full textCouture, V. Chapdelaine, S. Roy, M. S. Langer, and R. Mann. "Principal Components Analysis of Optical Snow." In British Machine Vision Conference 2004. British Machine Vision Association, 2004. http://dx.doi.org/10.5244/c.18.82.
Full textPakatci, Isa Kemal, Wei Wang, and Leonard McMillan. "Gene set analysis using principal components." In the First ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1854776.1854822.
Full textAllen, Genevera I., and Michael Weylandt. "Sparse and Functional Principal Components Analysis." In 2019 IEEE Data Science Workshop (DSW). IEEE, 2019. http://dx.doi.org/10.1109/dsw.2019.8755778.
Full textReports on the topic "Principal components analysis"
Zhu, Yifan. Principal Components Analysis of Discrete Datasets. Ames (Iowa): Iowa State University, January 2018. http://dx.doi.org/10.31274/cc-20240624-1152.
Full textAyres, João, Arturo Galindo, Santiago Novoa, and Victoria Nuguer. Inflation Dynamics in Latin America and the Caribbean. Inter-American Development Bank, March 2023. http://dx.doi.org/10.18235/0004751.
Full textHarter, 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.
Full textMARTIN, SHAWN B. Kernel Near Principal Component Analysis. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/810934.
Full textHamilton, James, and Jin Xi. Principal Component Analysis for Nonstationary Series. Cambridge, MA: National Bureau of Economic Research, January 2024. http://dx.doi.org/10.3386/w32068.
Full textAït-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.
Full textVelez, Gladis, and Ragvi Shah. Reorienting Smart City Metrics to Emphasize Resident Well-Being: A Disparity-Oriented Approach. University of Miami, 2022. http://dx.doi.org/10.33596/report-1.
Full textEick, Brian, Zachary Treece, Billie Spencer, Matthew Smith, Steven Sweeney, Quincy Alexander, and Stuart Foltz. Miter gate gap detection using principal component analysis. Engineer Research and Development Center (U.S.), June 2018. http://dx.doi.org/10.21079/11681/27365.
Full textZhao, George, Grang Mei, Bulent Ayhan, Chiman Kwan, and Venu Varma. DTRS57-04-C-10053 Wave Electromagnetic Acoustic Transducer for ILI of Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2005. http://dx.doi.org/10.55274/r0012049.
Full textFederer, W. T., C. E. McCulloch, and J. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis Using SYSTAT/FACTOR. Fort Belvoir, VA: Defense Technical Information Center, May 1987. http://dx.doi.org/10.21236/ada184920.
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