IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v7y2020i2d10.1007_s40745-020-00260-6.html
   My bibliography  Save this article

Performance of Some Factor Analysis Techniques

Author

Listed:
  • D. F. Nwosu

    (Federal Polytechnic, Nekede)

  • V. U. Ekhosuehi

    (University of Benin)

  • J. I. Mbegbu

    (University of Benin)

Abstract

This paper is a study on three multivariate data sets using some factor analysis techniques in the literature. The techniques are: the principal factor method (PFM), maximum likelihood factor analysis (MLFA), the classical principal component method (PCM) and the refined principal component method (rPCM). The computations are carried out using the statistical package for the social sciences (SPSS), Minitab and MATLAB. Findings reveal that the rPCM generates results as that of the PCM and that the rPCM and the PCM are more appropriate for exploratory factor analysis than the PFM and MLFA as the PFM and the MLFA may fail to converge or may yield a Heywood case.

Suggested Citation

  • D. F. Nwosu & V. U. Ekhosuehi & J. I. Mbegbu, 2020. "Performance of Some Factor Analysis Techniques," Annals of Data Science, Springer, vol. 7(2), pages 209-242, June.
    • Handle: RePEc:spr:aodasc:v:7:y:2020:i:2:d:10.1007_s40745-020-00260-6
    DOI: 10.1007/s40745-020-00260-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-020-00260-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-020-00260-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Boik, Robert J., 2013. "Model-based principal components of correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 310-331.
    2. Shen, Haipeng & Huang, Jianhua Z., 2008. "Sparse principal component analysis via regularized low rank matrix approximation," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1015-1034, July.
    3. Israr Ahmad Shah Hashmi & Arshad Ali Bhatti, 2019. "On the monetary measures of global liquidity," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-23, December.
    4. Torokhti, Anatoli & Friedland, Shmuel, 2009. "Towards theory of generic Principal Component Analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 661-669, April.
    5. Qi, Xin & Luo, Ruiyan & Zhao, Hongyu, 2013. "Sparse principal component analysis by choice of norm," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 127-160.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xin Qi & Ruiyan Luo, 2015. "Sparse Principal Component Analysis in Hilbert Space," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 270-289, March.
    2. Carrizosa, Emilio & Guerrero, Vanesa, 2014. "Biobjective sparse principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 151-159.
    3. Nickolay Trendafilov, 2014. "From simple structure to sparse components: a review," Computational Statistics, Springer, vol. 29(3), pages 431-454, June.
    4. Mitzi Cubilla-Montilla & Ana Belén Nieto-Librero & M. Purificación Galindo-Villardón & Carlos A. Torres-Cubilla, 2021. "Sparse HJ Biplot: A New Methodology via Elastic Net," Mathematics, MDPI, vol. 9(11), pages 1-15, June.
    5. Yixuan Qiu & Jing Lei & Kathryn Roeder, 2023. "Gradient-based sparse principal component analysis with extensions to online learning," Biometrika, Biometrika Trust, vol. 110(2), pages 339-360.
    6. Jushan Bai & Serena Ng, 2020. "Simpler Proofs for Approximate Factor Models of Large Dimensions," Papers 2008.00254, arXiv.org.
    7. Thomas Despois & Catherine Doz, 2023. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 533-555, June.
    8. Jin-Xing Liu & Yong Xu & Chun-Hou Zheng & Yi Wang & Jing-Yu Yang, 2012. "Characteristic Gene Selection via Weighting Principal Components by Singular Values," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    9. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
    10. Merola, Giovanni Maria & Chen, Gemai, 2019. "Projection sparse principal component analysis: An efficient least squares method," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 366-382.
    11. Maurizio Vichi, 2017. "Disjoint factor analysis with cross-loadings," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 563-591, September.
    12. Mihee Lee & Haipeng Shen & Jianhua Z. Huang & J. S. Marron, 2010. "Biclustering via Sparse Singular Value Decomposition," Biometrics, The International Biometric Society, vol. 66(4), pages 1087-1095, December.
    13. Gautam Sabnis & Debdeep Pati & Anirban Bhattacharya, 2019. "Compressed Covariance Estimation with Automated Dimension Learning," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 466-481, December.
    14. Kostyantyn Anatolievich Malyshenko & Majid Mohammad Shafiee & Vadim Anatolievich Malyshenko & Marina Viktorovna Anashkina, 2023. "Dynamics of the securities market in the information asymmetry context: developing a methodology for emerging securities markets," Papers 2307.04140, arXiv.org.
    15. 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.
    16. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Working Papers halshs-02235543, HAL.
    17. Nerea González-García & Ana Belén Nieto-Librero & Purificación Galindo-Villardón, 2023. "CenetBiplot: a new proposal of sparse and orthogonal biplots methods by means of elastic net CSVD," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 5-19, March.
    18. Liu, Zhongkai & Song, Rui & Zeng, Donglin & Zhang, Jiajia, 2017. "Principal components adjusted variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 134-144.
    19. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore & Wing-Keung Wong, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    20. Roman A. Jandarov & Lianne A. Sheppard & Paul D. Sampson & Adam A. Szpiro, 2017. "A novel principal component analysis for spatially misaligned multivariate air pollution data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 3-28, January.

    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:spr:aodasc:v:7:y:2020:i:2:d:10.1007_s40745-020-00260-6. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.