Doctoral Student

M.Sc. Maxime Faymonville

Contact

TU Dortmund University
Department of Statistics
Chair of Business and Social Statistics
CDI Building, Room 4
44221 Dortmund
Germany

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

Contributing to the DFG project Model diagnostics for count time series (research grant JE932/1-1, project number 437270842)

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. arXiv.
Faymonville, M., Riffo, J., Rieger, J. and Jentsch, C. (2023). spINAR: An R Package for Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive (INAR) Models. arXiv.

Predictive inference for count data time series. SMSA 2024. Delft, Netherlands (03/2024).
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).
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).