Relevant bibliographies by topics / Principal components

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Principal components'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Principal components"

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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 (1992): 217–25. http://dx.doi.org/10.1002/cem.1180060406.

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Abegaz, Fentaw, Kridsadakorn Chaichoompu, Emmanuelle Génin, et al. "Principals about principal components in statistical genetics." Briefings in Bioinformatics 20, no. 6 (2018): 2200–2216. http://dx.doi.org/10.1093/bib/bby081.

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Abstract Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction,
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Haswell, S. J. "Principal Components." Analytica Chimica Acta 309, no. 1-3 (1995): 405–6. http://dx.doi.org/10.1016/0003-2670(95)90335-6.

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Miyazaki, Haruo, and Youichi Seki. "Principal Components and Principal Clusters." Journal of Information and Optimization Sciences 8, no. 2 (1987): 189–99. http://dx.doi.org/10.1080/02522667.1987.10698885.

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Fujiwara, 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.

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Saegusa, 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.

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Mertens, 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 (1996): 178. http://dx.doi.org/10.1007/bf00162530.

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Whitlark, David, and George H. Dunteman. "Principal Components Analysis." Journal of Marketing Research 27, no. 2 (1990): 243. http://dx.doi.org/10.2307/3172855.

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Ammann, Larry P. "Robust Principal Components." Communications in Statistics - Simulation and Computation 18, no. 3 (1989): 857–74. http://dx.doi.org/10.1080/03610918908812795.

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Franses, Philip Hans, and Eva Janssens. "Spurious principal components." Applied Economics Letters 26, no. 1 (2018): 37–39. http://dx.doi.org/10.1080/13504851.2018.1433292.

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Dissertations / Theses on the topic "Principal components"

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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.

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Mestrado em Finanças<br>Nesta 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
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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.

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This study is concerned in reducing high dimensionality problem of auxiliary variables in the calibration estimation with the presence of nonresponse. The calibration estimation is a weighting method assists to compensate for the nonresponse in the survey analysis. Calibration estimation using principal components (PCs) is new idea in the literatures. Principal component analysis (PCA) is used in reduction dimension of the auxiliary variables. PCA in calibration estimation is presented as an alternative method for choosing the auxiliary variables. In this study, simulation on the real data is
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Sun, Linjuan. "Simple principal components." Thesis, Open University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429528.

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Duras, 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.

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The focus of this thesis is the common principal component (CPC) model, the generalization of principal components to several populations. Common principal components refer to a group of multidimensional datasets such that their inner products share the same eigenvectors and are therefore simultaneously diagonalized by a common decorrelator matrix. Common principal component analysis is essentially applied in the same areas and analysis as its one-population counterpart. The generalization to multiple populations comes at the cost of being more mathematically involved, and many problems in the
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Brubaker, S. Charles. "Extensions of principal components analysis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29645.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009.<br>Committee 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.
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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.

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Includes summary.<br>Includes bibliographical references.<br>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 detect
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Zavala, Frank Alcorta. "Principals' Perceptions of the Most Important Components in an Effective Principal Preparation Program." ScholarWorks, 2014. https://scholarworks.waldenu.edu/dissertations/26.

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Researchers in educational leadership have identified a need to improve principal preparation programs to meet today's educational demands. According to school administrators in the local area, not all leadership preparation programs used the same pedagogies to prepare future leaders, and principals were critical of existing leadership practices. School districts, students, parents, and community stakeholders would benefit from well-prepared administrators who can apply the most effective habits of principalship. The conceptual framework of the study was derived from J. Davis and Jazzar's 7 ha
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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.

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Uddin, Mudassir. "Interpretation of results from simplified principal components." Thesis, University of Aberdeen, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301216.

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Linear multivariate statistical methods are widely used for analysing data sets which consist of a large number of variables. These techniques, which include principal component analysis, factor analysis, canonical correlation analysis, redundancy analysis and discriminant analysis, all produce a set of new variables, commonly called 'factors', according to some criterion which differs for different techniques. Among these techniques, principal component analysis is one of the most popular techniques used for reducing the dimensions of the multivariate data set. In many applications, when Prin
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Neuenschwander, 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.

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Books on the topic "Principal components"

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Dunteman, George H. Principal components analysis. Sage Publications, 1989.

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Dunteman, George H. Principal components analysis. Sage Publications, 1989.

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Dunteman, George. Principal Components Analysis. SAGE Publications, Inc., 1989. http://dx.doi.org/10.4135/9781412985475.

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Jolliffe, I. T. Principal component analysis. 2nd ed. Springer, 2010.

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Jackson, J. Edward. A user's guide to principal components. Wiley, 1991.

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LeBlanc, Michael R. Adaptive principal surfaces. University of Toronto, Dept. of Statistics, 1991.

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Flury, Bernhard. Common principal components and related multivariate models. Wiley, 1988.

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Hyvarinen, Aapo. Independent component analysis. J. Wiley, 2001.

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Juha, Karhunen, and Oja Erkki, eds. Independent component analysis. J. Wiley, 2001.

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Kiers, Henk A. L. Three-way methods for the analysis of qualitative and quantitative two-way data. DSWO Press, University of Leiden, 1989.

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Book chapters on the topic "Principal components"

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Kloek, T. "Principal Components." In Time Series and Statistics. Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_27.

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Falk, Michael, Frank Marohn, and Bernward Tewes. "Principal Components." In Foundations of Statistical Analyses and Applications with SAS. Birkhäuser Basel, 1995. http://dx.doi.org/10.1007/978-3-0348-8195-1_8.

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Brown, Charles E. "Principal Components." In Applied Multivariate Statistics in Geohydrology and Related Sciences. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-80328-4_9.

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Kloek, T. "Principal Components." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_1776-1.

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Kloek, T. "Principal Components." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_1776.

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Hatanaka, Michio, and Hiroshi Yamada. "Principal Components." In Co-trending: A Statistical System Analysis of Economic Trends. Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-65912-9_4.

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Hijab, Omar. "Principal Components." In Math for Data Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89707-8_3.

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Jolicoeur, Pierre. "Principal components or principal axes." In Introduction to Biometry. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4777-8_32.

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Hilbert, Sven, and Markus Bühner. "Principal Components Analysis." In Encyclopedia of Personality and Individual Differences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1340.

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Härdle, Wolfgang, and Léopold Simar. "Principal Components Analysis." In Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_9.

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Conference papers on the topic "Principal components"

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SAUCIER, ANTOINE. "LOCALIZED PRINCIPAL COMPONENTS." In Conference on Fractals 2002. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777720_0028.

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SAUCIER, ANTOINE. "MULTISCALE PRINCIPAL COMPONENTS." In Fractals and Related Phenomena in Nature. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702746_0024.

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Bishop, 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.

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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.

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Aquilina, 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.

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Boutsidis, 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. Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973730.61.

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Foi, 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.

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Firmansyah, 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.

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Wei-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.

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Hoang, 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.

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Reports on the topic "Principal components"

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Chen, Xiaohong, Jose A. Scheinkman, and Lars Peter Hansen. Principal components and the long run. Institute for Fiscal Studies, 2009. http://dx.doi.org/10.1920/wp.cem.2009.0709.

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Neumann, Michael, and Hans Schneider. Principal Components of Minus M-Matrices. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada232354.

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Zhu, Yifan. Principal Components Analysis of Discrete Datasets. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-1152.

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Liu, Yong, and Harel Shouval. Principal Components of Natural Images: An Analytical Solution. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada264800.

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Ayres, João, Arturo Galindo, Santiago Novoa, and Victoria Nuguer. Inflation Dynamics in Latin America and the Caribbean. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004751.

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We perform a principal component analysis of the inflation dynamics in Latin America and the Caribbean to assess the recent surge in inflation across the region. The principal component accounts for 57% of the variation in inflation in the last 17 years, and it is highly correlated to the principal components of inflation of country groups outside the region, especially post-COVID-19. Global factors such as US inflation, commodity prices, and international shipping costs can account for at least one-third of the variation of the principal component. The analysis implies that external factors a
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Fekedulegn, 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. U.S. Department of Agriculture, Forest Service, Northeastern Research Station, 2002. http://dx.doi.org/10.2737/ne-rp-721.

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Iurasova, Olga, Larysa Ivashko, Oleksandr Maksymov, and Julia Maksymova. Impact of Return on Education on Economic Growth in EU Countries. Vilnius Business College, 2024. https://doi.org/10.57005/ab.2024.2.6.

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The objective of this article is to assess the impact of returns on the education and professional skills of workers on economic growth in EU countries. Based on open data, two principal components were formed to identify the aggregated influence of selected indicators on GDP growth. These principal components allow for the evaluation of the degree of influence of education and professional skills of workers on GDP growth for each country. Countries were clustered according to the degree of influence of the obtained principal components on the level of economic development using the k-means me
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Harter, 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, 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0041.1705.

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The 2015 Residential Energy Consumption Survey design called for stratification of primary sampling units to improve estimation. Two methods of defining strata from multiple stratification variables were proposed, leading to this investigation. All stratification methods use stratification variables available for the entire frame. We reviewed textbook guidance on the general principles and desirable properties of stratification variables and the assumptions on which the two methods were based. Using principal components combined with cluster analysis on the stratification variables to define s
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Ferreira, Clodomiro, and Stefano Pica. Households’ subjective expectations: disagreement, common drivers and reaction to monetary policy. Banco de España, 2024. http://dx.doi.org/10.53479/38316.

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Using granular data on household subjective expectations for several countries, we uncover a robust positive reaction of inflation expectations to a contractionary monetary policy shock, a result at odds with standard equilibrium theories with nominal rigidities. We then investigate what lies behind such result. Although households disagree, their expectations are correlated in the cross-section. Two principal components account for a significant portion of the variance of all expectations. These components capture households’ perceptions of the sources of macroeconomic dynamics, with the firs
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Velez, 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.

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This paper applies a disparity-oriented focus to promote human-centered solutions to smart city planning efforts. For five metropolitan areas (San Jose, Miami, New York, Denver, and Seattle) we explored three smart city domains (socioeconomics, public transit access, and digital divide), identified candidate indicators for each domain using publicly available data, and mapped composite measures generated using principal components analysis. The study identifies areas that may be most and least likely to benefit from smart city investments. Reorienting solutions can ultimately increase communit
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