M.Sc. Maxime Faymonville

• since January 2021: Research Associate and Doctoral Student at the Chair of Business and Social Statistics
• 2023: Study Coordinator of the Department of Statistics
• 2018-2020: Student and Research Assistant at the Chair of Business and Social Statistics
• 2018-2020: M.Sc. Statistics with minor in Econometrics, TU Dortmund University
  Master Thesis: Bootstrap-based prediction for INAR processes
• 2015-2018: B.Sc. Statistics with minor in Econometrics, TU Dortmund University

GitHub profile

• spINAR (github)

• Faymonville, M., Riffo, J., Rieger, J. and Jentsch, C. (2024). spINAR: An R Package for Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive (INAR) Models. Journal of Open Source Software, 9(97), 5386. DOI.
• Aleksandrov, B., Weiß, C.H., Nik, S., Faymonville, M. and Jentsch, C. (2023): Modelling and Diagnostic Tests for Poisson and Negative-binomial Count Time Series. Metrika. DOI.
• Weiß, C.H., Aleksandrov, B., Faymonville, M. and Jentsch, C. (2023): Partial Autocorrelation Diagnostics for Count Time Series. Entropy 2023, 25(1), 105. DOI.
• Aleksandrov, B., Weiß, C.H., Jentsch, C. and Faymonville, M. (2022). Novel Goodness-of-Fit Tests for Binomial Count Time Series. Statistics, 56(5), 957-990. DOI.
• Faymonville, M., Jentsch, C., Weiß, C.H. und Aleksandrov, B. (2022). Semiparametric Estimation of INAR Models using Roughness Penalization. Statistical Methods & Applications 32(2), 365-400. DOI.

Recent Submissions

• Faymonville, M., Jentsch, C. and Weiß, C.H. (2024). Semi-parametric Goodness-of-fit Testing for INAR Models. pdf.

• Goodness-of-fit testing for INAR models. Women in Mathematics 2025. Stockholm, Sweden (05/2025).
• Model diagnostics and inference for count time series. Kolloquium des Institutes für Mathematische Stochastik. Braunschweig, Germany (04/2025), (invited).
• Predictive inference for discrete-valued time series. GPSD 2025. Dresden, Germany (03/2025).
• Predictive inference for discrete-valued time series. CMS 2024. London, Great Britain (12/2024).
• Joint vector-autoregressive modeling of real- and integer-valued time series with full autoregressive parameter range. IMS 2024. Bochum, Germany (08/2024).
• Predictive inference for count data time series. SMSA 2024. Delft, Netherlands (03/2024), (invited).
• Goodness-of-fit testing for INAR models. CFE 2023. Berlin, Germany (12/2023).
• Goodness-of-fit testing for INAR models. Kolloquium der Fächergruppe Mathematik und Statistik. Hamburg, Germany (10/2023), (invited).
• Predictive inference for count data time series. Statistische Woche 2023. Dortmund, Germany (09/2023).
• Joint semiparametric INAR bootstrap inference for model coefficients and innovation parameters. GPSD 2023. Essen, Germany (03/2023).
• Joint semiparametric INAR bootstrap inference for model coefficients and innovation parameters. CFE 2022. London, Great Britain (12/2022).
• Joint semiparametric INAR bootstrap inference for model coefficients and innovation parameters. Statistische Woche 2022. Münster, Germany (09/2022).
• Semiparametric estimation of INAR models using roughness penalization. DAGStat 2022. Hamburg, Germany (03/2022).

• Advanced Linear Models (SuSe 2025)
• Asymptotic Theory (WiSe 2024/25)
• Bootstrap Methods (SuSe 2024)
• Econometrics (SuSe 2023)
• Vector and Matrix Calculus (WiSe 2022/23)
• Econometrics (SuSe 2022)
• Bootstrap Methods (WiSe 2021/22)
• Econometrics (SuSe 2021)
• Financial Econometrics (WiSe 2020/21)
• Linear Models (SuSe 2019)
• Analysis I Tutorial for Statisticians (WiSe 2017/18)
• Analysis II Tutorial for Statisticians (SuSe 2017)
• Analysis I Tutorial for Statisticians (WiSe 2016/17)