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
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(2022): Estimation of Asymmetric Spatial Autoregressive Dependence on Irregular Lattices., Symmetry 14(7)
DOI: https://doi.org/10.3390/sym14071474 -
(2022): Statistical monitoring of models based on artificial intelligence., arXiv preprint.
arXiv: 2209.07436 -
(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.
arXiv: 2011.06080 -
(2022): A general framework for spatial GARCH models., Stat Papers
DOI: https://doi.org/10.1007/s00362-022-01357-1 -
(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 -
(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 -
(2021): Spatiotemporal variable selection and air quality impact assessment of COVID-19 lockdown, Spatial Statistics, 100549.
DOI: https://doi.org/10.1016/j.spasta.2021.100549 -
(2021): Online network monitoring, Statistical Methods & Applications, Special Issue on Network Modelling (online first).
DOI: https://doi.org/10.1007/s10260-021-00589-z -
(2021): Directional spatial autoregressive dependence in the conditional first- and second-order moments, Spatial Statistics (online first)
DOI: https://doi.org/10.1016/j.spasta.2020.100490 -
(2021): Estimation of the spatial weighting matrix for regular lattice data -- An adaptive lasso approach with cross-sectional resampling, Environmetrics (online first)
DOI: https://doi.org/10.1002/env.2705 -
(2021): Impact of academic authorship characteristics on article citations, REVSTAT (online first). More info
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(2021): Statistical Analysis of Beach Profile Evolution and External Influences: Applying a Spatiotemporal Functional Approach, Coastal Engineering 170
DOI: https://doi.org/10.1016/j.coastaleng.2021.103999 -
(2020): Spatial Statistics, or how to extract knowledge from data, In Handbook of Big Geospatial Data. Springer Handbook Series in Computer Science. More info
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(2020): Parallelized Monitoring of Dependent Spatiotemporal Processes, Frontiers of Statistical Quality Control 13. More info
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(2020): Spatiotemporal procedures for the statistical surveillance of spatial autoregressive models with heavy tails, Communications in Statistics - Simulation and Computation (online first)
DOI: 10.1080/03610918.2020.1779294 -
(2019): Estimation of Anisotropic, Time‐Varying Spatial Spillovers of Fine Particulate Matter Due to Wind Direction, Geographical Analysis
DOI: 10.1111/gean.12205 -
(2019): Stochastic properties of spatial and spatiotemporal ARCH models, Statistical Papers, 1-16
DOI: 10.1007/s00362-019-01106-x -
(2019): spGARCH: An R-Package for Spatial and Spatiotemporal ARCH and GARCH models, The R-Journal More info
DOI: 10.32614/RJ-2019-053 -
(2018): Discussion of “Statistical methods for network surveillance” by Daniel Jeske, Nathaniel Stevens, Alexander Tartakovsky, and James Wilson, Applied Stochastic Models in Business and Industry 34(4). pp. 452-456
DOI: 10.1002/asmb.2360 -
(2018): Spatiotemporal analysis of German real-estate prices, The Annals of Regional Science Volume 60, Issue 1, pp 41–72
DOI: https://rdcu.be/bdG3n -
(2018): Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity, Spatial Statistics Volume 26, August 2018, Pages 125-145
DOI: 10.1016/j.spasta.2018.07.005 -
(2018): Verfahren zur Überwachung räumlicher autoregressiver Prozesse mit externen Regressoren, AStA Wirtschafts- und Sozialstatistisches Archiv Volume 12, Issue 2, pp 107–133
DOI: https://rdcu.be/bdG0S -
(2017): A note on efficient simulation of multidimensional spatial autoregressive processes, Communications in Statistics - Simulation and Computation Volume 46, 2017 - Issue 6
DOI: 10.1080/03610918.2015.1122050 -
(2016): Bayes’sche Statistik in der Dienstleistungsforschung, AStA Wirtschafts- und Sozialstatistisches Archiv Volume 10, Issue 4, pp 247–267
DOI: https://rdcu.be/bdG4t -
(2016): Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models, Biometrical Journal 58(5). pp. 1113-1137
DOI: 10.1002/bimj.201500148 -
(2016): Control charts for multivariate spatial autoregressive models, AStA Advances in Statistical Analysis Volume 101, Issue 1, pp 67–94
DOI: https://rdcu.be/bdG1S
Begutachtete Konferenzbeiträge
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(2019): Modeling Spatial Dependence in Local Risks and Uncertainties, Proceedings of the 29th European Safety and Reliability Conference More info
DOI: 10.3850/978-981-11-2724-3_0890-cd -
(2019): Statistical analysis of Sylt’s coastal profiles using a spatiotemporal functional model, Smart Statistics for Smart Applications, Pearson Proceedings, pp. 331-338 More info
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(2015): Simultaneous surveillance of means and covariances of spatial models, Springer Proceedings in Mathematics & Statistics, vol. 122, pp. 271-281
DOI: 10.1007/978-3-319-13881-7_30
ISBN: 978-3-319-13881-7 -
(2010): Evaluation of Innovative Potential (Оценка инновационности страны), 16th International Conference in Economics for Young Researches “Companies and Reforms in Russia
Lehrbücher
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(2017): Arbeitsbuch der Angewandten Statistik, SpringerGabler
Diskussionspapier
Software und Daten
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(2018): spGARCH: An R-Package for Spatial and Spatiotemporal ARCH models
