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

Listed author(s):
  • Matilde Trevisani
  • Arjuna Tuzzi

    ()

Registered author(s):

    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

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    File URL: http://hdl.handle.net/10.1007/s11135-014-0050-7
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    Article provided by Springer in its journal Quality & Quantity.

    Volume (Year): 49 (2015)
    Issue (Month): 3 (May)
    Pages: 1287-1304

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    Handle: RePEc:spr:qualqt:v:49:y:2015:i:3:p:1287-1304
    DOI: 10.1007/s11135-014-0050-7
    Contact details of provider: Web page: http://www.springer.com

    Order Information: Web: http://www.springer.com/economics/journal/11135

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    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, 03.
    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.
    3. 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.
    4. 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, 06.
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