IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v49y2015i3p1287-1304.html
   My bibliography  Save this article

A portrait of JASA: the History of Statistics through analysis of keyword counts in an early scientific journal

Author

Listed:
  • Matilde Trevisani
  • Arjuna Tuzzi

Abstract

The words that occur in papers published by the journals of an old and prestigious scientific society like the American Statistical Association portray the most relevant research interests of a discipline and the recurrence of words over time show fashions, forgotten topics and new emerging subjects, that is, the history of a discipline at a glance. In this study a set of keywords occurred in the titles of papers published in the period 1888–2012 by the Journal of the American Statistical Association and its predecessors are examined over time in order to retrieve those which appeared in the past and which are today the research fields covered by Statistics, from the viewpoints of both methods and application domains. The existence of a latent temporal pattern in keywords’ occurrences is explored by means of (lexical) correspondence analysis and clusters of keywords portraying similar temporal patterns are identified by functional (textual) data analysis and model-based curve clustering. The analyses reveal a definite time dimension in topics and show that much of the History of Statistics may be gleaned by simply reading the titles of papers through an explorative correspondence analysis. However, the functional approach and model-based curve clustering turn out to be better in tracing and comparing the individual temporal evolution of keywords, despite some computational and theoretical limitations. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Matilde Trevisani & Arjuna Tuzzi, 2015. "A portrait of JASA: the History of Statistics through analysis of keyword counts in an early scientific journal," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1287-1304, May.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:3:p:1287-1304
    DOI: 10.1007/s11135-014-0050-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-014-0050-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-014-0050-7?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. M. Giacofci & S. Lambert-Lacroix & G. Marot & F. Picard, 2013. "Wavelet-Based Clustering for Mixed-Effects Functional Models in High Dimension," Biometrics, The International Biometric Society, vol. 69(1), pages 31-40, March.
    2. Florian Reithinger & Wolfgang Jank & Gerhard Tutz & Galit Shmueli, 2008. "Modelling price paths in on‐line auctions: smoothing sparse and unevenly sampled curves by using semiparametric mixed models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 127-148, April.
    3. Mario A. Maggioni & T. Erika Uberti & Francesca Gambarotto, 2009. "Mapping the Evolution of "Clusters": A Meta-analysis," Working Papers 2009.74, Fondazione Eni Enrico Mattei.
    4. Jeffrey S. Morris & Raymond J. Carroll, 2006. "Wavelet‐based functional mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 179-199, April.
    5. David J. Bartholomew, 1995. "What is Statistics?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 1-20, January.
    6. Jeffrey S. Morris & Philip J. Brown & Richard C. Herrick & Keith A. Baggerly & Kevin R. Coombes, 2008. "Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models," Biometrics, The International Biometric Society, vol. 64(2), pages 479-489, June.
    7. Efstathios Stamatatos, 2009. "A survey of modern authorship attribution methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 538-556, March.
    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. Madison Giacofci & Sophie Lambert-Lacroix & Franck Picard, 2018. "Minimax wavelet estimation for multisample heteroscedastic nonparametric regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 238-261, January.
    2. Chau, Van Vinh & von Sachs, Rainer, 2016. "Functional mixed effects wavelet estimation for spectra of replicated time series," LIDAM Discussion Papers ISBA 2016013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    4. M. Giacofci & S. Lambert-Lacroix & G. Marot & F. Picard, 2013. "Wavelet-Based Clustering for Mixed-Effects Functional Models in High Dimension," Biometrics, The International Biometric Society, vol. 69(1), pages 31-40, March.
    5. Dongik Jang & Hee-Seok Oh & Philippe Naveau, 2017. "Identifying local smoothness for spatially inhomogeneous functions," Computational Statistics, Springer, vol. 32(3), pages 1115-1138, September.
    6. Mark J. Meyer & Brent A. Coull & Francesco Versace & Paul Cinciripini & Jeffrey S. Morris, 2015. "Bayesian function‐on‐function regression for multilevel functional data," Biometrics, The International Biometric Society, vol. 71(3), pages 563-574, September.
    7. Zhu, Hongxiao & Morris, Jeffrey S. & Wei, Fengrong & Cox, Dennis D., 2017. "Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 88-101.
    8. Lin Zhang & Veerabhadran Baladandayuthapani & Hongxiao Zhu & Keith A. Baggerly & Tadeusz Majewski & Bogdan A. Czerniak & Jeffrey S. Morris, 2016. "Functional CAR Models for Large Spatially Correlated Functional Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 772-786, April.
    9. Ana-Maria Staicu & Yingxing Li & Ciprian M. Crainiceanu & David Ruppert, 2014. "Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 932-949, December.
    10. John A. D. Aston & Jeng‐Min Chiou & Jonathan P. Evans, 2010. "Linguistic pitch analysis using functional principal component mixed effect models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 297-317, March.
    11. Hongxiao Zhu & Philip J. Brown & Jeffrey S. Morris, 2012. "Robust Classification of Functional and Quantitative Image Data Using Functional Mixed Models," Biometrics, The International Biometric Society, vol. 68(4), pages 1260-1268, December.
    12. Fabienne Comte & Adeline Samson, 2012. "Nonparametric estimation of random-effects densities in linear mixed-effects model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 951-975, December.
    13. Nils-Axel M?rner, 2018. "Evaluation of the Performance and Efficiency of the Automated Linguistic Features for Author Identification in Short Text Messages Using Different Variable Selection Techniques," Studies in Media and Communication, Redfame publishing, vol. 6(2), pages 83-102, December.
    14. Selene Yue Xu & Sandahl Nelson & Jacqueline Kerr & Suneeta Godbole & Eileen Johnson & Ruth E. Patterson & Cheryl L. Rock & Dorothy D. Sears & Ian Abramson & Loki Natarajan, 2019. "Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 403-421, July.
    15. Maryam Ebrahimpour & Tālis J Putniņš & Matthew J Berryman & Andrew Allison & Brian W-H Ng & Derek Abbott, 2013. "Automated Authorship Attribution Using Advanced Signal Classification Techniques," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
    16. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    17. Yukun Zhang & Haocheng Li & Sarah Kozey Keadle & Charles E. Matthews & Raymond J. Carroll, 2019. "A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 465-476, July.
    18. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.
    19. Bruno Scarpa & David B. Dunson, 2009. "Bayesian Hierarchical Functional Data Analysis Via Contaminated Informative Priors," Biometrics, The International Biometric Society, vol. 65(3), pages 772-780, September.
    20. Chen, Di-Rong & Cheng, Kun & Liu, Chao, 2022. "Framelet block thresholding estimator for sparse functional data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

    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:qualqt:v:49:y:2015:i:3:p:1287-1304. 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.