IDEAS home Printed from https://ideas.repec.org/p/eei/rpaper/eeri_rp_2016_24.html
   My bibliography  Save this paper

A Bayesian Reversible Jump Piecewise Hazard approach for modelling rate changes in mass shootings

Author

Listed:
  • Andrew G. Chapple

Abstract

Time to event data for econometric tragedies, like mass shootings, have largely been ignored from a changepoint analysis standpoint. We outline a technique for modelling economic changepoint problems using a piece- wise constant hazard model to explain different economic phenomenon. Specifically, we investigate the rates of mass shootings in the United States since August 20th 1982 as a case study to examine changes in rates of these terrible events in an attempt to connect changes to the shooter’s covariates or policy and societal changes.

Suggested Citation

  • Andrew G. Chapple, 2016. "A Bayesian Reversible Jump Piecewise Hazard approach for modelling rate changes in mass shootings," EERI Research Paper Series EERI RP 2016/24, Economics and Econometrics Research Institute (EERI), Brussels.
    • Handle: RePEc:eei:rpaper:eeri_rp_2016_24
    as

    Download full text from publisher

    File URL: http://www.eeri.eu/documents/wp/EERI_RP_2016_24.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kyu Ha Lee & Sebastien Haneuse & Deborah Schrag & Francesca Dominici, 2015. "Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(2), pages 253-273, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrew G. Chapple, 2018. "Modeling ISIL terror attacks and their fatality rates with a Bayesian reversible jump marked point process," EERI Research Paper Series EERI RP 2018/09, Economics and Econometrics Research Institute (EERI), Brussels.

    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. Fei Jiang & Sebastien Haneuse, 2017. "A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 112-129, March.
    2. Harrison T. Reeder & Junwei Lu & Sebastien Haneuse, 2023. "Penalized estimation of frailty‐based illness–death models for semi‐competing risks," Biometrics, The International Biometric Society, vol. 79(3), pages 1657-1669, September.
    3. Chapple, Andrew G. & Vannucci, Marina & Thall, Peter F. & Lin, Steven, 2017. "Bayesian variable selection for a semi-competing risks model with three hazard functions," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 170-185.
    4. Yang Li & Hao Liu & Xiaoshen Wang & Wanzhu Tu, 2022. "Semi‐parametric time‐to‐event modelling of lengths of hospital stays," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1623-1647, November.
    5. Bo-Hong Wu & Hirofumi Michimae & Takeshi Emura, 2020. "Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty–copula model," Computational Statistics, Springer, vol. 35(4), pages 1525-1552, December.
    6. Lea Kats & Malka Gorfine, 2023. "An accelerated failure time regression model for illness–death data: A frailty approach," Biometrics, The International Biometric Society, vol. 79(4), pages 3066-3081, December.
    7. Kyu Ha Lee & Virginie Rondeau & Sebastien Haneuse, 2017. "Accelerated failure time models for semi‐competing risks data in the presence of complex censoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1401-1412, December.
    8. Daniel Nevo & Deborah Blacker & Eric B. Larson & Sebastien Haneuse, 2022. "Modeling semi‐competing risks data as a longitudinal bivariate process," Biometrics, The International Biometric Society, vol. 78(3), pages 922-936, September.
    9. Yen‐Tsung Huang, 2021. "Causal mediation of semicompeting risks," Biometrics, The International Biometric Society, vol. 77(4), pages 1143-1154, December.
    10. Xifen Huang & Jinfeng Xu & Hao Guo & Jianhua Shi & Wenjie Zhao, 2022. "An MM Algorithm for the Frailty-Based Illness Death Model with Semi-Competing Risks Data," Mathematics, MDPI, vol. 10(19), pages 1-13, October.
    11. Mário de Castro & Ming‐Hui Chen & Yuanye Zhang & Anthony V. D'Amico, 2020. "A Bayesian multi‐risks survival (MRS) model in the presence of double censorings," Biometrics, The International Biometric Society, vol. 76(4), pages 1297-1309, December.
    12. Yen‐Tsung Huang, 2021. "Rejoinder to “Causal mediation of semicompeting risks”," Biometrics, The International Biometric Society, vol. 77(4), pages 1170-1174, December.
    13. Il Do Ha & Liming Xiang & Mengjiao Peng & Jong-Hyeon Jeong & Youngjo Lee, 2020. "Frailty modelling approaches for semi-competing risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 109-133, January.

    More about this item

    Keywords

    Time-to-event Data; Bayesian Analyses; Piecewise Exponential; Reversible Jump; Mass Shooting.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:eei:rpaper:eeri_rp_2016_24. 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: Julia van Hove (email available below). General contact details of provider: https://edirc.repec.org/data/eeriibe.html .

    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.