Dr Ali Shariati

Dr Ali Shariati

Research Associate
  • PhD in Statistics, Macquarie University, Australia.
  • MRes in Statistics, Macquarie University, Australia.
  • MSc in Mathematical Statistics, Ferdowsi University of Mashhad, Iran.
  • BSc in Statistics, University of Birjand, Iran.
Science
School of Mathematics & Statistics

Ali Shariati is a statistician at the School of Mathematics and Statistics. His expertise lies in developing statistical methodologies for inference on real-world phenomena, with a focus on medical research and epidemiology. His research spans Survival Analysis, Stochastic Processes, Point Processes, Nonparametric Statistics and Empirical Likelihood.

Ali’s work focuses on developing statistical methods for complex and challenging data scenarios. In Survival Analysis, he investigates functionals of empirical processes, particularly in data subject to selection bias and informative censoring which has significant applications in Prevalent Cohort studies. Additionally, he is working on statistical inference for Stochastic Point Processes, specifically Hawkes Processes, under incomplete data settings, with applications across various disciplines. His work often incorporates Statistics Computing.

Ali earned a Ph.D. in Statistics from Macquarie University, with his thesis titled Nonparametric Inference from Censored Prevalent Cohort Data with an Application to Survival with Dementia. Part of his PhD research was conducted at McGill University, in Canada, under the supervision of Prof. Masoud Asgharian.

Location
School of Mathematics and Statistics Anita B. Lawrence Centre (East) Room: 5102 UNSW Sydney Sydney NSW 2052 Australia
  • Journal articles | 2023
    Shariati A; Doosti H; Fakoor V; Asgharian M, 2023, 'Uniform confidence bands for hazard functions from censored prevalent cohort survival data', Electronic Journal of Statistics, 17, pp. 1807 - 1847, http://dx.doi.org/10.1214/23-EJS2133
    Journal articles | 2022
    Amiri N; Fakoor V; Sarmad M; Shariati A, 2022, 'Empirical likelihood analysis for accelerated failure time model using length-biased data', Statistics, 56, pp. 578 - 597, http://dx.doi.org/10.1080/02331888.2022.2077334
    Journal articles | 2020
    Fakoor V; Ajami M; Jahanshahi SMA; Shariati A, 2020, 'A density-based empirical likelihood ratio approach for goodness-of-fit tests in decreasing densities', Statistics Optimization and Information Computing, 8, pp. 66 - 79, http://dx.doi.org/10.19139/soic-2310-5070-707
    Journal articles | 2019
    Heidari R; Fakoor V; Shariati A, 2019, 'A presmooth estimator of unbiased distributions with length-biased data', Mathematical Sciences, 13, pp. 317 - 323, http://dx.doi.org/10.1007/s40096-019-00301-z
    Journal articles | 2018
    Fakoor V; Shariati A; Sarmad M, 2018, 'The MRL function inference through empirical likelihood in length-biased sampling', Journal of Statistical Planning and Inference, 196, pp. 115 - 131, http://dx.doi.org/10.1016/j.jspi.2017.11.001

  • Faculty of Science and Engineering Excellence Award 2024 - Student Nominated Award for Teaching, Macquarie University, Sydney, Australia.
  • Vice-Chancellor's Commendation for Academic Excellence, Doctor of Philosophy in Statistics, Macquarie University, Sydney, Australia.