1. Cartea, S. Jaimungal, and J. Ricci: Buy Low Sell High: A High Frequency Trading Perspective
  2. C. Chang, Using Mixture Models for Clustering, 13-Oct-2015. [Online]. Available: http://tinyheero.github.io/2015/10/13/mixture-model.html.
  3. F. Lorenzen: Analysis of Order Clustering Using High Frequency Data: A Point Process Approach
  4. J. Carlsson, M. Foo, H. Lee, H. Shek: High Frequency Trade Prediction with Bivariate Hawkes Process.
  5. J. Heusser, “Bitcoin Trade Arrival as Self-Exciting Process,” Bitcoin Trade Arrival as Self-Exciting Process, 08-Sep-2013. [Online]. Available: http://jheusser.github.io/2013/09/08/hawkes.html.
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  8. Poisson Process charateristics taken from H. Pishro-Nik, “Basic Concepts of the Poisson Process,” Probability Course. [Online]. Available: https://www.probabilitycourse.com/chapter11/11_1_2_basic_concepts_of_the_poisson_process.php.

  9. Q. Zhao, M. Erdogdu, H. He, A. Rajaraman, and J. Leskovec, “SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity,” Aug. 2015.
  10. R. Traylor, “Poisson Process and Data Loss,” The Math Citadel, 17-Oct-2017. [Online]. Available: http://www.themathcitadel.com/2017/10/17/poisson-processes-and-data-loss/.
  11. T. Huffine, “Hawkes Process Using MLE,” Github. 21-Jul-2015
  12. V. Filimonov, and D. Sornette: Apparent criticality and calibration issues in the Hawkes self-excited point process model: application to high-frequency financial data
  13. Y. P. Raykov, A. Boukouvalas, and M. Little, “What to do when K-means clustering fails: a simple yet principled alternative algorithm,” Semantic Scholar.
  14. Z. Khraibani and H. Khraibani, “Self-Exciting Point Process to Study the Evolution of the Attack Terrorism,” International Journal of Statistics and Applications, 2016. [Online]. Available: http://article.sapub.org/10.5923.j.statistics.20160606.04.html.