1. The impact of government actions and risk perception on the promotion of self-protective behaviors during the COVID-19 pandemic. Alvarez-Galvez, J., Anastasiou, A., Lamnisos, D., Constantinou, M., Nicolaou, C., Papacostas, S., Vasiliou, V. S., McHugh, L., Lubenko, J., Ruiz, F. J., Paez-Blarrina, M., Montesinos, F., Valdivia-Salas, S., Merwin, R. M., Karekla, M., Gloster, A. T. & Kassianos, A. P. (2022+). In submission.

  2. Detecting change-points in noisy data sequences with continuous piecewise structures. Ma, Y., Anastasiou, A., Wang, T., Montiel, F. (2022+). In submission.

  3. Finite sample distributional error bounds for empirical autocovariances and cross-covariances. Anastasiou, A. & Kley, T. (2022+). In submission.

  4. Stein’s Method Meets Statistics: A Review of Some Recent Developments. Anastasiou, A., Barp, A., Briol, F-X., Ebner, B., Gaunt, R. E., Ghaderinezhad, F., Gorham, J., Gretton, A., Ley, C., Liu, Q., Mackey, L., Oates, C. J., Reinert, G. & Swan, Y. (2022+) arXiv:2105.03481 [stat.ME]. Under revision.

  5. Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity. Anastasiou, A., Cribben, I. & Fryzlewicz, P. (2022). Medical Image Analysis, 75.

  6. Wasserstein distance error bounds for the multivariate normal approximation of the maximum likelihood estimator. Anastasiou, A. & Gaunt, R. E. (2021). Electronic Journal of Statistics, 15, No.2, 5758-5810.

  7. Detecting multiple generalized change-points by isolating single ones. Anastasiou, A. & Fryzlewicz, P. (2021). Metrika, doi:10.1007/s00184-021-00821-6.

  8. Modeling the first wave of Covid-19 pandemic in the Republic of Cyprus. Agapiou, S., Anastasiou, A., Baxevani, A., Nicolaides, C., Hadjigeorgiou, G., Christofides, T., Constantinou, E., Nikolopoulos, G. & Fokianos, K. (2021). Scientific Reports, 11, Article no: 7342 

  9. Bounds for the asymptotic distribution of the likelihood ratio. Anastasiou, A. & Reinert, G. (2020). Annals of Applied Probability, 30, No. 2, 608-643.

  10. Multivariate normal approximation of the maximum likelihood estimator via the delta method. Anastasiou, A. & Gaunt, R. E. (2020). Brazilian Journal of Probability and Statistics, 34, 136-149.

  11. Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. Anastasiou, A. & Balasubramanian, K. & Erdogdu, M. (2019). Proceedings of the Conference on Learning Theory (COLT) 2019.

  12. Assessing the multivariate normal approximation of the maximum likelihood estimator from high-dimensional, heterogeneous data. Anastasiou, A. (2018). Electronic Journal of Statistics, 12, No. 2, 3794-3828.

  13. Bounds for the asymptotic normality of the maximum likelihood estimator using the Delta method. Anastasiou, A. & Ley, C. (2017).  ALEA, Lat. Am. J. Probab. Math. Stat. 14, 153-171.

  14. Bounds for the normal approximation of the maximum likelihood estimator from m-dependent random variables. Anastasiou, A. (2017). Statistics & Probability Letters 129, 171-181.

  15. Bounds for the normal approximation of the maximum likelihood estimator. Anastasiou, A. & Reinert, G. (2017). Bernoulli 23, 191-218. 

- Claude Bernard -

“The joy of discovery is certainly the liveliest that the mind of man can ever feel”

PUBLICATIONS AND PREPRINTS

SOFTWARE

  1. IDetect: Isolate-Detect Methodology for Multiple Change-Point Detection. R package. [CRAN]

  2. breakfast: Computationally Efficient Multiple Change-Point Detection and Estimation with a Broad Range of Methods. R package. [CRAN]

  3. ccid: a change-point detection method for estimating dynamic functional connectivity. R package. [CRAN]