Kybernetika 60 no. 1, 110-124, 2024

A model and application of binary random sequence with probabilities depending on history

Petr Volf and Tomáš KouřimDOI: 10.14736/kyb-2024-1-0110

Abstract:

This paper presents a model of binary random sequence with probabilities depending on previous sequence values as well as on a set of covariates. Both these dependencies are expressed via the logistic regression model, such a choice enables an easy and reliable model parameters estimation. Further, a model with time-depending parameters is considered and method of solution proposed. The main objective is then the application dealing with both artificial and real data cases, illustrating the method of model evaluation and its use.

Keywords:

recurrent events, discrete time process, binary sequence, varying probabilities, logistic regression, time-dependent parameters

Classification:

62J12, 62N02, 60G50

References:

  1. R. A. Davis and H. Liu: Theory and inference for a class of observation-driven models with application to time series of counts. Statistica Sinica 26 (2016), 1673-1707.   DOI:10.5705/ss.2014.145t
  2. A. G. Hawkes: Spectra of some self-exciting and mutually exciting point processes. Biometrika 58 (1971), 83-90.   DOI:10.1093/biomet/58.1.83
  3. G. M. Fitzmaurice, N. M. Laird and J. H. Ware: Applied Longitudinal Analysis. Wiley, Hoboken 2004.   CrossRef
  4. J. D. Kalbfleisch and R. L. Prentice: The Statistical Analysis of Failure Time Data. Wiley, New York 2002.   CrossRef
  5. T. Kouřim: Random walks with memory applied to grand slam tennis matches modeling. In: Proc. MathSport International 2019 Conference (e-book). Propobos Publications 2019, pp. 220-227.   CrossRef
  6. T. Kouřim and P. Volf: Discrete random processes with memory: Models and applications. Appl. Math.65 (2020), 271-286.   DOI:10.21136/AM.2020.0335-19
  7. T. A. Möller: Self-exciting threshold models for time series of counts with a finite range. Stoch. Models 32 (2016), 77-98.   DOI:10.1080/15326349.2015.1085319
  8. S. A. Murphy and P. K. Sen: Time-dependent coefficients in a Cox-type regression model. Stoch. Proc. Appl. 39 (1991), 153-180.   DOI:10.1016/0304-4149(91)90039-f
  9. P. Volf and T. Kouřim: A model of discrete random walk with history-dependent transition probabilities. Commun. Statist. - Theory and Methods 52 (2023), 5173-5186.   DOI:10.1080/03610926.2021.2004425
  10. L. T. Wei, D. Y. Lin and L. Weissfeld: Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J. Amer. Statist. Assoc. 84 (1989), 1065-1073.   DOI:10.1080/01621459.1989.10478873
  11. Ch. H. Weiss: An Introduction to Discrete Valued Time Series. Wiley, New York 2018.   CrossRef
  12. R. Winkelmann: Econometric Analysis of Count Data. Springer, Berlin 2008.   CrossRef