Oguamalam, J., Filzmoser, P., Hron, K., Menafoglio, A., & Radojicic, U. (2024). Robust functional PCA for density data. arXiv. https://doi.org/10.34726/8739

Radojicic, U., Mayrhofer, M., & Filzmoser, P. (2024, December 17). Expainable outlier detection for multivariate random processes with separable covariance structure. ICSDS2024, Nizza, France.

Radojičić, U., Mayrhofer, M., & Filzmoser, P. (2024, October 25). Explainable Outlier Detection for Multivariate Functional Data. Turku Applied Mathematics and Statistics Seminar, Finland.

Radojicic, U., Mayrhofer, M., & Filzmoser, P. (2024, October 3). Explainable outlier detection for multivariate functional data based on a functional Mahalanobis distance. Statistical seminar of Department of Mathematics, Croatia.

Oguamalam, J., Radojičić, U., & Filzmoser, P. (2024, September 5). Functional Outlier Detection. SMPS2024, Salzburg, Austria.

Mayrhofer, M., Radojicic, U., & Filzmoser, P. (2024, August 13). Robust covariance estimation for matrix-valued data. Bernoulli-ims 11th World Congress in Probability and Statistics, Bochum, Germany.

Mayrhofer, M., Radojičić, U., & Filzmoser, P. (2024, July 31). Robust PCA and explainable outlier detection for multivariate functional data based on a functional Mahalanobis distance. ICORS meets DSSV 2024, United States of America (the).

Oguamalam, J., Radojičić, U., & Filzmoser, P. (2024). Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data. Technometrics, 66(4), 588–599. https://doi.org/10.1080/00401706.2024.2336542

Mayrhofer, M., Radojicic, U., & Filzmoser, P. (2024, April 4). Explainable anomaly detection using Shapley values. Statistiktage 2024, Wien, Austria.

Oguamalam, J., Radojicic, U., & Filzmoser, P. (2024). Robust covariance estimation and functional anomaly detection based on the Minimum Regularized Covariance Trace estimator. In PROGRAM AND ABSTRACTS - Austrian Statistical Days 2024. Austrian Statistical Days 2024, Wien, Austria.

Matys Grygar, T., Radojičić, U., Pavlu, I., Greven, S., Nešlehová, J. G., Tůmová, Š., & Hron, K. (2024). Exploratory functional data analysis of multivariate densities for the identification of agricultural soil contamination by risk elements. Journal of Geochemical Exploration, 259, Article 107416. https://doi.org/10.1016/j.gexplo.2024.107416

Mayrhofer, M., Radojicic, U., & Filzmoser, P. (2024). A minimum covariance determinant approach for matrix-variate data. In Statistische Woche 2024: Book of Abstracts (pp. 88–88).

Möttönen, J., Nordhausen, K., Oja, H., & Radojicic, U. (2024). The Asymptotic Properties of the One-Sample Spatial Rank Methods. In M. Barigozzi, S. Hörmann, & D. Paindaveine (Eds.), Recent Advances in Econometrics and Statistics: Festschrift in Honour of Marc Hallin (pp. 49–69). Springer. https://doi.org/10.1007/978-3-031-61853-6_3

Filzmoser, P., Mayrhofer, M., Radojicic, U., & Lewitschnig, H. (2023). Explainable outlier identification for matrix-valued observations. In Book of Abstracts : International Conference on Data Science : ICDS 2023 : Multidimensional Perspectives: From Statistical Learning to Data Science Applications (pp. 13–13).

Oguamalam, J., Radojicic, U., & Filzmoser, P. (2023). Functional Outlier Detection based on the Minimum Regularized Covariance Trace Estimator. In Book of abstracts: Joint conference of Data Science, Statistics & Visualisation and the European Conference on Data Analysis (pp. 102–102).

Oguamalam, J., Radojicic, U., & Filzmoser, P. (2023). Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data. In Book of Abstracs - Olomoucian Days of Applied Mathematics ODAM 2023 (pp. 57–57).

Mayrhofer, M., Radojicic, U., Lewitschnig, H., & Filzmoser, P. (2023, May 24). Outlier detection and explanation for matrix-valued data. International Conference on Robust Statistics (ICORS) - 2023, Toulouse, France.

Archimbaud, A., Drmac, Z., Nordhausen, K., Radojičić, U., & Ruiz-Gazen, A. (2023). Numerical Considerations and a new implementation for invariant coordinate selection. SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE, 5(1), 97–121. https://doi.org/10.1137/22M1498759

Radojičić, U., Nordhausen, K., & Taskinen, S. (2022). Singular Spectrum Analysis. In Encyclopedia of Mathematical Geosciences. https://doi.org/10.1007/978-3-030-26050-7_294-1

Radojicic, U., Lietzen, N., Nordhausen, K., & Virta, J. (2021). Dimension Estimation in Two-Dimensional PCA. In 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA). 12th Int´l Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia. https://doi.org/10.1109/ispa52656.2021.9552114

Radojicic, U., Nordhausen, K., & Virta, J. (2021). Kurtosis-based projection pursuit for matrix-valued data. Twenty-eight International Workshop on Matrices and Statistics, Manipal, India, India.

Radojicic, U., Nordhausen, K., & Virta, J. (2021). Large-sample properties of blind estimation of the linear discriminant using projection pursuit. Online Conference Data Science, Statistics & Visualization (DSSV) 2021, Rotterdam, online, Netherlands (the).

Radojičić, U., & Nordhausen, K. (2021). Least Absolute Value. In Encyclopedia of Mathematical Geosciences. https://doi.org/10.1007/978-3-030-26050-7_177-1

Radojičić, U., Nordhausen, K., & Virta, J. (2021). Large-sample properties of blind estimation of the linear discriminant using projection pursuit. Electronic Journal of Statistics, 15(2). https://doi.org/10.1214/21-ejs1956

Radojicic, U., & Nordhausen, K. (2020). Non-Gaussian Component Analysis: Testing the Dimension of the Signal Subspace. Online International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), London, United Kingdom of Great Britain and Northern Ireland (the).

Radojicic, U., Lietzen, N., Nordhausen, K., & Virta, J. (2020). Order Determination for Matrix-valued Observations Using Data Augmentation. Statistical Seminar, J. J. Strossmayer University of Osijek Department of Mathematics, Osijek, Croatia.

Radojicic, U., Nordhausen, K., & Oja, H. (2020). Notion of Information and Independent Component Analysis. Online Conference Data Science, Statistics & Visualization (DSSV) 2020, Durham, United Kingdom of Great Britain and Northern Ireland (the).

Radojicic, U., Nordhausen, K., & Oja, H. (2020). Notion of Information and Independent Component Analysis. Online International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), London, United Kingdom of Great Britain and Northern Ireland (the).

Radojicic, U., Oja, H., & Nordhausen, K. (2020). Notion of Information and Independent Component Analysis. Statistical Seminar, J. J. Strossmayer University of Osijek Department of Mathematics, Osijek, Croatia.

Radojičić, U., & Nordhausen, K. (2020). Non-Gaussian Component Analysis: Testing the Dimension of the Signal Subspace. In M. Maciak, M. Pesta, & M. Schindler (Eds.), Analytical Methods in Statistics (pp. 101–123). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-48814-7_6

Radojičić, U., Nordhausen, K., & Oja, H. (2020). Notion of Information and Independent Component Analysis. Applications of Mathematics, 65(3), 311–330. https://doi.org/10.21136/am.2020.0326-19