Advertisement

Frontiers in Latent Variable Analysis

  • J. Christopher Westland
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 22)

Abstract

Advances in computing have made possible new latent variable methods that were not possible even 5 years ago. Network analysis and machine learning offer some of the most compelling computationally intensive approaches to data analysis. They have achieved objectives—playing chess and go, face and voice recognition, intelligent response to queries, and more—that statisticians might only have dreamt of 20 years ago. One surprising consequence of this intense investment in computationally intensive data analytics has been the surprise appearance of latent constructs that researchers had not hypothesized in advance. These emergent properties represent a whole new landscape for inquiry, theory-building, and latent variable analysis.

References

  1. Barabási, Albert-László, Zoltán Dezső, Erzsébet Ravasz, Soon-Hyung Yook, and Zoltán Oltvai. 2003. “Scale-Free and Hierarchical Structures in Complex Networks.” AIP Conference Proceedings 661 (1): 1–16.Google Scholar
  2. ———. 1963a. “A Note on the Exact Finite Sample Frequency Functions of Generalized Classical Linear Estimators in a Leading Three-Equation Case.” Journal of the American Statistical Association 58 (301): 161–171.Google Scholar
  3. Calhoun, Craig. 2007. Nations Matter: Culture, History and the Cosmopolitan Dream. Abingdon: Routledge.CrossRefGoogle Scholar
  4. Chollet, François, and Joseph J. Allaire. 2018. Deep Learning with R, Ch.5.4. Shelter Island: Manning Publications Company.Google Scholar
  5. de Sola Pool, I., and M. Kochen. 1979. “Contacts and Influences.” Social Networks 1 (1): 5.MathSciNetCrossRefGoogle Scholar
  6. Fruchterman, Thomas M.J., and Edward M. Reingold. 1991. “Graph Drawing by Force-Directed Placement.” Software: Practice and Experience 21 (11): 1129–1164.Google Scholar
  7. Goh, Kwang-Il, Michael E. Cusick, David Valle, Barton Childs, Marc Vidal, and Albert-László Barabási. 2007. “The Human Disease Network.” Proceedings of the National Academy of Sciences 104 (21): 8685–8690.CrossRefGoogle Scholar
  8. Kobourov, Stephen G. 2012. “Spring Embedders and Force Directed Graph Drawing Algorithms.” arXiv Preprint arXiv:1201.3011.Google Scholar
  9. McCullagh, Peter, and John A. Nelder. 1989. Generalized Linear Models. Vol. 37. Boca Raton: CRC Press.CrossRefGoogle Scholar
  10. Pryor, Robert. 1982. “Values, Preferences, Needs, Work Ethics, and Orientations to Work: Toward a Conceptual and Empirical Integration.” Journal of Vocational Behavior 20 (1): 40–52.CrossRefGoogle Scholar
  11. Schonhoff, Thomas, and Arthur Giordano. 2006. Detection and Estimation Theory. New Jersey: Prentice Hall.Google Scholar
  12. Travers, Jeffrey, and Stanley Milgram. 1967. “The Small World Problem.” Psychology Today 1 (1): 61–67.Google Scholar
  13. ———. 1977. “An Experimental Study of the Small World Problem.” In Social Networks, 179–197. New York: Elsevier.CrossRefGoogle Scholar
  14. Watts, Duncan J., and Steven H. Strogatz. 1998. “Collective Dynamics of ‘Small-World’ networks.” Nature 393 (6684): 440.CrossRefGoogle Scholar
  15. Wedderburn, Robert W.M. 1974. “Quasi-Likelihood Functions, Generalized Linear Models, and the Gauss—Newton Method.” Biometrika 61 (3): 439–447.MathSciNetzbMATHGoogle Scholar
  16. Wittfogel, Karl A. 1957. “Chinese Society: An Historical Survey.” The Journal of Asian Studies 16 (3): 343–364.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information & Decision SystemsUniversity of Illinois at ChicagoChicagoUSA

Personalised recommendations