Philipp Otto

War Juniorprofessor für “Big Geospatial Data”. Seine Forschungsthemen liegen im Bereich statistischer Lernverfahren, statistischer, raum-zeitlicher Modelle, insb. GARCH Modelle, Umweltstatistik sowie statistischer Netzwerkmodellierung. 
Seit dem 1.9.2023 ist Philipp Otto Reader in Statistics and Data Analytics an der Universität Glasgow.

Publikationen

Begutachtete Zeitschriftenartikel und Buchkapitel

  • Malinovskaya, A., Mozharovskyi, P. and Otto, P. (2023): Statistical process monitoring of artificial neural networks.Technometrics, 66(1), pp. 104-117
    DOI: doi.org/10.1080/ 00401706.2023.2239886
  • Harke, F. H., Merk, M., Otto, P. (2022): Estimation of Asymmetric Spatial Autoregressive Dependence on Irregular Lattices.Symmetry 14(7)
    DOI: https://doi.org/10.3390/sym14071474
  • Malinovskaya, A., Mozharovskyi, P., & Otto, P. (2022): Statistical monitoring of models based on artificial intelligence.arXiv preprint.
    arXiv: 2209.07436
  • Malinovskaya, A., Otto, P., Peters, T. (2022): Statistical learning for change point and anomaly detection in graphs.Artificial Intelligence, Big Data and Data Science in Statistics (pp. 85-109). Springer, Cham.
    DOI: doi.org/10.1007/978-3-031-07155-3_4
    arXiv: 2011.06080
  • Otto, P., Schmid, W. (2022): A general framework for spatial GARCH models.Stat Papers
    DOI: https://doi.org/10.1007/s00362-022-01357-1
  • Otto, P., Steinert, R. (2022): Estimation of the Spatial Weighting Matrix for Spatiotemporal Data under the Presence of Structural Breaks.Journal of Computational and Graphical Statistics
    DOI: 10.1080/10618600.2022.2107530
  • Piter, A., Otto, P., Alkhatib, H. (2022): The Helsinki Bike-Sharing Systems - Insights gained from a spatiotemporal functional model.Journal of the Royal Statistical Society Series A
    DOI: https://doi.org/10.1111/rssa.12834
  • Fassò, A., Maranzano, P., & Otto, P. (2021): Spatiotemporal variable selection and air quality impact assessment of COVID-19 lockdownSpatial Statistics, 100549.
    DOI: https://doi.org/10.1016/j.spasta.2021.100549
  • Malinovskaya, A., Otto, P. (2021): Online network monitoringStatistical Methods & Applications, Special Issue on Network Modelling (online first).
    DOI: https://doi.org/10.1007/s10260-021-00589-z
  • Merk, M., Otto, P. (2021): Directional spatial autoregressive dependence in the conditional first- and second-order momentsSpatial Statistics (online first)
    DOI: https://doi.org/10.1016/j.spasta.2020.100490
  • Miryam S. Merk, Philipp Otto (2021): Estimation of the spatial weighting matrix for regular lattice data -- An adaptive lasso approach with cross-sectional resamplingEnvironmetrics (online first)
    DOI: https://doi.org/10.1002/env.2705
  • Otto, P., Otto, P. (2021): Impact of academic authorship characteristics on article citationsREVSTAT (online first). Weitere Informationen
  • Otto, P., Piter, A., Gijsman, R. (2021): Statistical Analysis of Beach Profile Evolution and External Influences: Applying a Spatiotemporal Functional ApproachCoastal Engineering 170
    DOI: https://doi.org/10.1016/j.coastaleng.2021.103999
  • Antoniuk, A., Merk, M., Otto, P. (2020): Spatial Statistics, or how to extract knowledge from dataIn Handbook of Big Geospatial Data. Springer Handbook Series in Computer Science. Weitere Informationen
  • Malinovskaya, A., Killick, R., Leeming, K., and Otto, P. (2020): Statistical monitoring of European cross-border physical electricity flows using novel temporal edge network processes.
    DOI: doi.org/10.48550/arXiv.2312.16357
    arXiv: 2312.16357
  • Otto, P. (2020): Parallelized Monitoring of Dependent Spatiotemporal ProcessesFrontiers of Statistical Quality Control 13. Weitere Informationen
  • Robert Garthoff, Philipp Otto (2020): Spatiotemporal procedures for the statistical surveillance of spatial autoregressive models with heavy tailsCommunications in Statistics - Simulation and Computation (online first)
    DOI: 10.1080/03610918.2020.1779294
  • Miryam S. Merk, Philipp Otto (2019): Estimation of Anisotropic, Time‐Varying Spatial Spillovers of Fine Particulate Matter Due to Wind DirectionGeographical Analysis
    DOI: 10.1111/gean.12205
  • Otto, P., Schmid, W. & Garthoff, R. (2019): Stochastic properties of spatial and spatiotemporal ARCH modelsStatistical Papers, 1-16
    DOI: 10.1007/s00362-019-01106-x
  • Philipp Otto (2019): spGARCH: An R-Package for Spatial and Spatiotemporal ARCH and GARCH modelsThe R-Journal Weitere Informationen
    DOI: 10.32614/RJ-2019-053
  • Philipp Otto, Wolfgang Schmid (2018): Discussion of “Statistical methods for network surveillance” by Daniel Jeske, Nathaniel Stevens, Alexander Tartakovsky, and James WilsonApplied Stochastic Models in Business and Industry 34(4). pp. 452-456
    DOI: 10.1002/asmb.2360
  • Philipp Otto, Wolfgang Schmid (2018): Spatiotemporal analysis of German real-estate pricesThe Annals of Regional Science Volume 60, Issue 1, pp 41–72
    DOI: https://rdcu.be/bdG3n
  • Philipp Otto, Wolfgang Schmid, Robert Garthoff (2018): Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticitySpatial Statistics Volume 26, August 2018, Pages 125-145
    DOI: 10.1016/j.spasta.2018.07.005
  • Robert Garthoff, Philipp Otto (2018): Verfahren zur Überwachung räumlicher autoregressiver Prozesse mit externen RegressorenAStA Wirtschafts- und Sozialstatistisches Archiv Volume 12, Issue 2, pp 107–133
    DOI: https://rdcu.be/bdG0S
  • Philipp Otto (2017): A note on efficient simulation of multidimensional spatial autoregressive processesCommunications in Statistics - Simulation and Computation Volume 46, 2017 - Issue 6
    DOI: 10.1080/03610918.2015.1122050
  • Anna-Liesa Lange, Philipp Otto (2016): Bayes’sche Statistik in der DienstleistungsforschungAStA Wirtschafts- und Sozialstatistisches Archiv Volume 10, Issue 4, pp 247–267
    DOI: https://rdcu.be/bdG4t
  • Philipp Otto, Wolfgang Schmid (2016): Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive modelsBiometrical Journal 58(5). pp. 1113-1137
    DOI: 10.1002/bimj.201500148
  • Robert Garthoff, Philipp Otto (2016): Control charts for multivariate spatial autoregressive modelsAStA Advances in Statistical Analysis Volume 101, Issue 1, pp 67–94
    DOI: https://rdcu.be/bdG1S

Begutachtete Konferenzbeiträge

  • Philipp Otto (2019): Modeling Spatial Dependence in Local Risks and UncertaintiesProceedings of the 29th European Safety and Reliability Conference Weitere Informationen
    DOI: 10.3850/978-981-11-2724-3_0890-cd
  • Rik Gijsman, Philipp Otto, Torsten Schlurmann, Jan Visscher (2019): Statistical analysis of Sylt’s coastal profiles using a spatiotemporal functional modelSmart Statistics for Smart Applications, Pearson Proceedings, pp. 331-338 Weitere Informationen
  • Garthoff, R., Otto, P. (2015): Simultaneous surveillance of means and covariances of spatial modelsSpringer Proceedings in Mathematics & Statistics, vol. 122, pp. 271-281
    DOI: 10.1007/978-3-319-13881-7_30
    ISBN: 978-3-319-13881-7
  • Otto, P. (2010): Evaluation of Innovative Potential (Оценка инновационности страны)16th International Conference in Economics for Young Researches “Companies and Reforms in Russia

Lehrbücher

  • Otto, Philipp, Lange, Anna-Liesa (2017): Arbeitsbuch der Angewandten StatistikSpringerGabler

Diskussionspapier

  • Philipp Otto, Wolfgang Schmid (2019): Spatial and Spatiotemporal GARCH Models -- A Unified Approach
    arXiv: 1908.08320

Software und Daten

  • Otto, Philipp (2018): spGARCH: An R-Package for Spatial and Spatiotemporal ARCH models