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Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime

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  • Taddy, Matthew A.

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  • Taddy, Matthew A., 2010. "Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1403-1417.
  • Handle: RePEc:bes:jnlasa:v:105:i:492:y:2010:p:1403-1417
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    Cited by:

    1. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    2. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    3. Athanasios Kottas, 2018. "Discussion of paper “nonparametric Bayesian inference in applications” by Peter Müller, Fernando A. Quintana and Garritt L. Page," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 219-225, June.
    4. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    5. Bassetti, Federico & Casarin, Roberto & Leisen, Fabrizio, 2014. "Beta-product dependent Pitman–Yor processes for Bayesian inference," Journal of Econometrics, Elsevier, vol. 180(1), pages 49-72.
    6. Božidar Popović & Saralees Nadarajah & Miroslav Ristić, 2013. "A new non-linear AR(1) time series model having approximate beta marginals," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 71-92, January.
    7. Luis E. Nieto-Barajas & Fernando A. Quintana, 2016. "A Bayesian Non-Parametric Dynamic AR Model for Multiple Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 675-689, September.
    8. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2018. "Bayesian Nonparametric Calibration and Combination of Predictive Distributions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 675-685, April.
    9. Luis E. Nieto-Barajas & Peter Müller & Yuan Ji & Yiling Lu & Gordon B. Mills, 2012. "A Time-Series DDP for Functional Proteomics Profiles," Biometrics, The International Biometric Society, vol. 68(3), pages 859-868, September.
    10. Monica Billio & Roberto Casarin & Matteo Iacopini, 2018. "Bayesian Markov Switching Tensor Regression for Time-varying Networks," Working Papers 2018:14, Department of Economics, University of Venice "Ca' Foscari".
    11. Jan Povala & Seppo Virtanen & Mark Girolami, 2020. "Burglary in London: insights from statistical heterogeneous spatial point processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1067-1090, November.
    12. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    13. Peluso, Stefano & Mira, Antonietta & Muliere, Pietro, 2015. "Reinforced urn processes for credit risk models," Journal of Econometrics, Elsevier, vol. 184(1), pages 1-12.
    14. Santitissadeekorn, N. & Short, M.B. & Lloyd, D.J.B., 2018. "Sequential data assimilation for 1D self-exciting processes with application to urban crime data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 163-183.

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