IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v16y1967i3p225-236.html
   My bibliography  Save this article

Two Case Studies in the Application of Principal Component Analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2985919
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2985919?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Choulakian, V., 2001. "Robust Q-mode principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 135-150, August.
    3. 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.
    4. 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.
    5. Carrizosa, Emilio & Guerrero, Vanesa, 2014. "Biobjective sparse principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 151-159.
    6. Doyo Enki & Nickolay Trendafilov, 2012. "Sparse principal components by semi-partition clustering," Computational Statistics, Springer, vol. 27(4), pages 605-626, December.
    7. 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.
    8. 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.
    9. 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.
    10. Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Jolliffe, Ian, 2022. "A 50-year personal journey through time with principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    17. 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.
    18. 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.
    19. Nickolay Trendafilov, 2014. "From simple structure to sparse components: a review," Computational Statistics, Springer, vol. 29(3), pages 431-454, June.
    20. 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.
    21. 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.
    22. Luca Scrucca, 2006. "Subset selection in dimension reduction methods," Quaderni del Dipartimento di Economia, Finanza e Statistica 23/2006, Università di Perugia, Dipartimento Economia.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:16:y:1967:i:3:p:225-236. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.