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Prof. Dr. Matthias Templ

Prof. Dr. Matthias Templ

Tätigkeiten an der FHNW

Dozent

Nur peer-reviewed

Keine peer-reviewed Inhalte verfügbar
Peer-reviewed

Thees, O., Novak, J., & Templ, M. (2024). Evaluation of synthetic data generators on complex tabular data. In J. Domingo-Ferrer & M. Önen (Eds.), Privacy in statistical databases. International Conference, PSD 2024, Antibes Juan-les-Pins, France, September 25–27, 2024, Proceedings (pp. 194–209). Springer. https://doi.org/10.1007/978-3-031-69651-0_13
Peer-reviewed

Filzmoser, P., & Templ, M. (2023). Prof. Rudolf Dutter (1946-2023): Ein Nachruf. Austrian Journal of Statistics, 52(3), 143–144. https://doi.org/10.17713/ajs.v52i3.1736
Peer-reviewed

Templ, M. (2023). Enhancing precision in large-scale data analysis: an innovative robust imputation algorithm for managing outliers and missing values. Mathematics, 11(12). https://doi.org/10.3390/math11122729
Peer-reviewed

Templ, M., Kanjala, C., & Siems, I. (2022). Privacy of study participants in open-access health and demographic surveillance system data. Requirements analysis for data anonymization. JMIR Public Health and Surveillance, 8(9). https://doi.org/10.2196/34472
Peer-reviewed

Templ, M., & Templ, B. (2022). Statistical analysis of chemical element compositions in food science: problems and possibilities. Molecules, 26(19), 5752. https://doi.org/10.3390/molecules26195752
Peer-reviewed

Templ, M., Gozzi, C., & Buccianti, A. (2022). A new version of the Langelier-Ludwig square diagram under a compositional perspective. Journal of Geochemical Exploration, 242(107048). https://doi.org/10.1016/j.gexplo.2022.107084
Peer-reviewed

Templ, M., & Sariyar, M. (2022). A systematic overview on methods to protect sensitive data provided for various analyses. International Journal of Information Security, 21, 1233–1246. https://doi.org/10.1007/s10207-022-00607-5
Templ, M. (2021). Artificial neural networks to impute rounded zeros in compositional data. In P. Filzmoser, K. Hron, J. A. Martín-Fernández, & J. Palarea-Albaladejo (Eds.), Advances in compositional data analysis. Festschrift in honour of Vera Pawlowsky-Glahn (pp. 163–187). Springer. https://doi.org/10.1007/978-3-030-71175-7_9
Peer-reviewed

Templ, B., Templ, M., Barbieri, R., Meier, M., & Zufferey, V. (2021). Coincidence of temperature extremes and phenological events of grapevines. Oeno One, 55(1), 367–383. https://doi.org/10.20870/OENO-ONE.2021.55.1.3187
Peer-reviewed

Lubbe, S., Filzmoser, P., & Templ, M. (2021). Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems, 210, 104248. https://doi.org/10.1016/J.CHEMOLAB.2021.104248
Templ, M. (2021). Can we ignore the compositional nature of compositional data by using deep learning aproaches? In C. Perna, N. Salvati, & F. Schirripa Spagnolo (Eds.), Book of short papers SIS 2021 (pp. 243–248). Pearson. https://irf.fhnw.ch/handle/11654/43326

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