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Two Case Studies in the Application of Principal Component Analysis

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  • J. N. R. Jeffers

Abstract

Two case studies of the application of principal component analysis to practical problems are presented, and it is suggested that there is a need for the extensive application of existing methods of multivariate analysis over a wide range of problems and subjects in order to test the practical value of the techniques.

Suggested Citation

  • J. N. R. Jeffers, 1967. "Two Case Studies in the Application of Principal Component Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(3), pages 225-236, November.
    • Handle: RePEc:bla:jorssc:v:16:y:1967:i:3:p:225-236
    DOI: 10.2307/2985919
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    Cited by:

    1. Elsy Garnica Olmos, 1996. "Principal component analysis of household budget," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 21(11), pages 55-90, January-D.
    2. Fadel Hamid Hadi ALHUSSEINI & Meshal Harbi ODAH, 2016. "Principal Component Regression For Tobit Model And Purchases Of Gold," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 10(1), pages 491-500, November.
    3. Ronald Gunderson & Pin Ng, 2006. "Summarizing the Effect of a Wide Array of Amenity Measures into Simple Components," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 79(2), pages 313-335, November.
    4. Choulakian, V., 2001. "Robust Q-mode principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 135-150, August.
    5. Seong-Yun Hong & Seonggook Moon & Sang-Hyun Chi & Yoon-Jae Cho & Jeon-Young Kang, 2022. "Local Sparse Principal Component Analysis for Exploring the Spatial Distribution of Social Infrastructure," Land, MDPI, vol. 11(11), pages 1-16, November.
    6. Lopamudra Banerjee, 2016. "Catastrophes And Consumption Failure," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 61(01), pages 1-25, March.
    7. Fernandez-Haddad, Zaira & Quiroga, Sonia, 2011. "Adaptation Of Mediterranean Crops To Water Pressure In The Ebro Basin: A Water Efficiency Index," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114358, European Association of Agricultural Economists.
    8. Jolliffe, Ian, 2022. "A 50-year personal journey through time with principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    9. Susan P Sparkes & Rifat Atun & Till Bӓrnighausen, 2019. "The impact of the Family Medicine Model on patient satisfaction in Turkey: Panel analysis with province fixed effects," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-13, January.
    10. Kohei Adachi & Nickolay T. Trendafilov, 2016. "Sparse principal component analysis subject to prespecified cardinality of loadings," Computational Statistics, Springer, vol. 31(4), pages 1403-1427, December.
    11. Zaijun Li & Jianquan Cheng & Qiyan Wu, 2016. "Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis," Letters in Spatial and Resource Sciences, Springer, vol. 9(3), pages 233-245, October.
    12. Carrizosa, Emilio & Guerrero, Vanesa, 2014. "Biobjective sparse principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 151-159.
    13. Yu Yu & Nita Umashankar & Vithala R. Rao, 2016. "Choosing the right target: Relative preferences for resource similarity and complementarity in acquisition choice," Strategic Management Journal, Wiley Blackwell, vol. 37(8), pages 1808-1825, August.
    14. Doyo Enki & Nickolay Trendafilov, 2012. "Sparse principal components by semi-partition clustering," Computational Statistics, Springer, vol. 27(4), pages 605-626, December.
    15. Davood Hajinezhad & Qingjiang Shi, 2018. "Alternating direction method of multipliers for a class of nonconvex bilinear optimization: convergence analysis and applications," Journal of Global Optimization, Springer, vol. 70(1), pages 261-288, January.
    16. Nickolay Trendafilov, 2014. "From simple structure to sparse components: a review," Computational Statistics, Springer, vol. 29(3), pages 431-454, June.
    17. Sabatier, Robert & Reynès, Christelle, 2008. "Extensions of simple component analysis and simple linear discriminant analysis using genetic algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4779-4789, June.
    18. Fonseca Luis & Fernandes Jorge & Ramos Sandra, 2019. "Enabling factors for the competitiveness of the Portuguese automotive industry," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 13(1), pages 1-12, May.
    19. Amir Beck & Yakov Vaisbourd, 2016. "The Sparse Principal Component Analysis Problem: Optimality Conditions and Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 170(1), pages 119-143, July.
    20. Luca Scrucca, 2006. "Subset selection in dimension reduction methods," Quaderni del Dipartimento di Economia, Finanza e Statistica 23/2006, Università di Perugia, Dipartimento Economia.
    21. Jinhak Kim & Mohit Tawarmalani & Jean-Philippe P. Richard, 2019. "Convexification of Permutation-Invariant Sets," Purdue University Economics Working Papers 1315, Purdue University, Department of Economics.
    22. Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.

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