IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v47y2013i2p1143-1161.html
   My bibliography  Save this article

A Bayesian analysis of suicide data

Author

Listed:
  • Rosalia Condorelli

Abstract

The identification of change points in a sequence of suicide rates is one of the fundamental aspects of Durkheim’s theory. The specification of a statistical standard suitable for this purpose is the main condition for making inferences about the causes of suicide with distinctive trends of persistency and variability just as Durkheim theorized. At present, the statistical ‘strategy’ employed by the French social scientist is too ‘rudimentary’. A hundred years later, I take the opportunity to test Durkheim’s theory through modern methodological instruments, specifically the Bayesian change-point analysis. First of all, I analyzed the same suicide data which Durkheim took into consideration. Change-point analysis corroborates the Durkheimian analysis revealing the same change-points identified by the author. Secondly, I analyzed Italian suicide rates from 1864 to 2005. The change-point analysis was very useful. Durkheim’s theory ‘works’ until 1961: suicides rates increased as industrial development increased. However, after 1961 and the economic boom, they declined, and when they began increasing again, after 1984, they did not reach the same level as before. This finding obliges us to ‘adjust’ the Durkheim’s theory giving space to Halbwach’s convergence law. Therefore, as high economic and social development levels are attained, suicide rates tend to level-off: People adapt to the stress of modernization associated to low social integration levels. Although we are more ‘egoist’, individualism does not destroy identity and the sense of life as Durkheim had maintained. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Rosalia Condorelli, 2013. "A Bayesian analysis of suicide data," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 1143-1161, February.
  • Handle: RePEc:spr:qualqt:v:47:y:2013:i:2:p:1143-1161
    DOI: 10.1007/s11135-011-9608-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-011-9608-9
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11135-011-9608-9?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. Ulrich Menzefricke, 1981. "A Bayesian Analysis of a Change in the Precision of a Sequence of Independent Normal Random Variables at an Unknown Time Point," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(2), pages 141-146, June.
    2. Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, June.
    3. Diaz, Joaquin, 1982. "Bayesian detection of a change of scale parameter in sequences of independent gamma random variables," Journal of Econometrics, Elsevier, vol. 19(1), pages 23-29, May.
    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. DAVID E. ALLEN & MICHAEL McALEER & ROBERT J. POWELL & ABHAY K. SINGH, 2018. "Non-Parametric Multiple Change Point Analysis Of The Global Financial Crisis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-23, June.
    2. Fitzpatrick, Matthew, 2014. "Geometric ergodicity of the Gibbs sampler for the Poisson change-point model," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 55-61.
    3. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
    4. Owyang, Michael T. & Piger, Jeremy & Wall, Howard J., 2008. "A state-level analysis of the Great Moderation," Regional Science and Urban Economics, Elsevier, vol. 38(6), pages 578-589, November.
    5. Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
    6. Michael W. Robbins & Colin M. Gallagher & Robert B. Lund, 2016. "A General Regression Changepoint Test for Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 670-683, April.
    7. Cathy W. S. Chen & Mike K. P. So, 2003. "Subset threshold autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 49-66.
    8. Fernando Ferraz do Nascimento & Wyara Vanesa Moura e Silva, 2017. "A Bayesian model for multiple change point to extremes, with application to environmental and financial data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2410-2426, October.
    9. Ľluboš Pástor & Robert F. Stambaugh, 2001. "The Equity Premium and Structural Breaks," Journal of Finance, American Finance Association, vol. 56(4), pages 1207-1239, August.
    10. Gordon, Stephen & Bélanger, Gilles, 1996. "Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes," L'Actualité Economique, Société Canadienne de Science Economique, vol. 72(1), pages 27-49, mars.
    11. Gary M. Koop & Simon M. Potter, 2004. "Forecasting and Estimating Multiple Change-point Models with an Unknown Number of Change-points," Discussion Papers in Economics 04/31, Division of Economics, School of Business, University of Leicester.
    12. Rotondi, R., 2002. "On the influence of the proposal distributions on a reversible jump MCMC algorithm applied to the detection of multiple change-points," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 633-653, September.
    13. David Ardia & Arnaud Dufays & Carlos Ordás Criado, 2024. "Linking Frequentist and Bayesian Change-Point Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1155-1168, October.
    14. Tian, Guo-Liang & Ng, Kai Wang & Tan, Ming, 2008. "EM-type algorithms for computing restricted MLEs in multivariate normal distributions and multivariate t-distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4768-4778, June.
    15. Li Zhaoyuan & Tian Maozai, 2017. "Detecting Change-Point via Saddlepoint Approximations," Journal of Systems Science and Information, De Gruyter, vol. 5(1), pages 48-73, February.
    16. Eric F. Lock & Nidhi Kohli & Maitreyee Bose, 2018. "Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 733-750, September.
    17. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 1057-1084.
    18. Tian, Guo-Liang & Ng, Kai Wang & Li, Kai-Can & Tan, Ming, 2009. "Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of Haemolytic uraemic syndrome," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3314-3323, July.
    19. R. Rotondi & E. Garavaglia, 2002. "Statistical Analysis of the Completeness of a Seismic Catalogue," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 25(3), pages 245-258, March.
    20. Griffin, J.E. & Steel, M.F.J., 2011. "Stick-breaking autoregressive processes," Journal of Econometrics, Elsevier, vol. 162(2), pages 383-396, June.

    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:47:y:2013:i:2:p:1143-1161. 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.