Relevant bibliographies by topics / Principal components

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Principal components'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 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 (July 1992): 217–25. http://dx.doi.org/10.1002/cem.1180060406.

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Abegaz, 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.

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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, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.
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Haswell, 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.

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Miyazaki, 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.

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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 (June 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 (May 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 (January 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 (February 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 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.<br>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.<br>info:eu-repo/semantics/publishedVersion
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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 used and nonresponse mechanism is applied on the sampled data. The calibration estimator is compared using different criteria such as varying the nonresponse rate and increasing the sample size. From the results, although the calibration estimation based on the principal components have reasonable outputs to use instead of the whole auxiliary variables for the means, the variance is very large compared with based on original auxiliary variables. Finally, we identified the principal component analysis is not efficient in the reduction of high dimensionality problem of auxiliary variables in the calibration estimation for large sample sizes.
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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 area remains to be solved. This thesis consists of three individual papers and an introduction chapter.In the first paper, the performance of two different estimation methods of the CPC model is compared for two real-world datasets and in a Monte Carlo simulation study. The second papers show that the orthogonal group and the Haar measure on this group plays an important role in PCA, both in single- and multi-population principal component analysis. The last paper considers using common principal component analysis as a tool for imposing restrictions on system-wise regression models. When the exogenous variables of a multi-dimensional model share common principal components, then each of the marginal models in the system is, up to their eigenvalues, identical. They henceform a class of regression models situated in between the classical seemingly unrelated regressions, where each set of explanatory variables is unique, and multivariate regression, where each marginal model shares the same common set of regressors.
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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 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.
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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 habits of an effective principal preparation program. For this qualitative case study, 16 principals were interviewed to find out which components of a principal preparation program they thought were the most important or had best prepared them for their positions. Analysis involved open coding, and resulting themes revealed that principals perceived the most important components to be a multisituational internship and extensive experience with school budget/finance. A professional development session was created to share interview responses with policymakers. Principal preparation programs that involve an in-depth internship and practice with school budget and finance could be used to assist policy makers in developing leadership training programs for future principals to improve student and school performance for school districts. This project study could foster social change with greater school success for students, resulting from improvement in leadership preparation programs.
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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 Principal Component Analysis (PCA) is performed on a large number of variables, the interpretation of the results is not simple. The derived eigenvectors of the sample covariance or correlation matrix are not necessarily in a simple form, with all coefficients either 'large' or 'negligible'. To aid interpretation, it is fairly common practice to rotate the retained set of components, often using orthogonal rotation. The purpose of rotation is to simplify structure, and thus to make it easier to interpret the low-dimensional space represented by the retained set of components. Thus, quantification of simplicity is a two step process. The first set involves the extraction of the feature from the data called components, while the second stage uses a rotation method to simplify the structure. One of the two main purposes of this thesis is to combine into one step these two separate stages of dimension reduction (finding the components) and simplification (rotation). This goal is achieved by combining these two objectives in the form of a single function leading to what we call Simplified Components (SCs). Another objective is to discover which of the many possible criteria suggested in factor analysis can be adopted in the proposed procedure of SCs. Thus, a simplified one-step procedure of SCs is proposed, using four measures of simplicity, namely varimax, quartimax, orthomax and equamax indices.
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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. Newbury Park: Sage Publications, 1989.

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Dunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.

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Dunteman, 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.

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

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

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

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

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Hyvarinen, Aapo. Independent component analysis. New York: J. Wiley, 2001.

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

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Kiers, Henk A. L. Three-way methods for the analysis of qualitative and quantitative two-way data. Leiden, Netherlands: 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, 204–7. London: 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, 321–61. Basel: 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, 103–11. Berlin, Heidelberg: 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, 1–4. London: 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, 10747–49. London: 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, 25–27. Tokyo: 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, 121–86. Cham: 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, 280–302. Boston, MA: 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, 4030–34. Cham: 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, 233–73. Berlin, Heidelberg: 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. Philadelphia, PA: 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, May 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. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada232354.

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Zhu, Yifan. Principal Components Analysis of Discrete Datasets. Ames (Iowa): Iowa State University, January 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. Fort Belvoir, VA: Defense Technical Information Center, May 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, March 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 are major drivers of the surge in inflation in the region post-COVID-19 lockdowns.
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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. Newtown Square, PA: 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, December 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 method. As a result, 20 EU countries were divided into three clusters. The first cluster consists of developed countries with a high share of innovations. The sec-ond cluster includes developed countries with a lower share of innovations than the first cluster and moderate values of the return on education. The third cluster includes countries with a large share of non-innovative sec-tors. Subsequently, a regression model was constructed to analyse the influence of each component on GDP growth. Based on the comparison of coefficient values, it was concluded that the increase in the relationship between the level of education and GDP growth in EU countries is observed with the increase in the innovative and technological components of the economy.
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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, May 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 strata focuses on relationships among stratification variables. Decision trees, regressions, and correlation approaches focus more on relationships between the stratification variables and prior outcome data, which may be available for just a sample of units. Using both principal components/cluster analysis and decision trees, we stratified primary sampling units for the 2009 Residential Energy Consumption Survey and compared the resulting strata.
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Ferreira, Clodomiro, and Stefano Pica. Households’ subjective expectations: disagreement, common drivers and reaction to monetary policy. Madrid: Banco de España, November 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 first capturing either a supply-side view or an overall dislike for inflation, and the second component reflecting a perception about demand pressures. This structure of disagreement is stable across countries and over time and does not vary with demographic or socioeconomic characteristics. We then use these insights to identify two common factors driving expectations over time. These factors are consistent with a narrative based on perceived supply-side inflationary pressures after the invasion of Ukraine in February 2022, as well as with the overall downward inflation dynamics intensified by the reaction of the ECB.
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MARTIN, SHAWN B. Kernel Near Principal Component Analysis. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/810934.

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