Anomaly detection using Machine Learning

Depth Statistics

Functional Data

Kalman filter and state-space models

Online aggregation of experts for prediction

Multivariate longitudinal data analysis

Multivariate linear mixed-effects model (parameter estimation and variables selection)

W. Zhou, E. Adjakossa, C. Lévy-Leduc, N. Ternès, 2021. Sign Consistency of the Generalized Elastic Net Estimator. (Submitted; see the Arxiv version here)

Adjakossa, E., Goude, Y. & Wintenberger, O. Kalman recursions Aggregated Online. Stat Papers (2023). [Arxiv ](see some related R codes here)

Adjakossa, E., Hounkonnou, N. & Nuel, G. (2019). Computationally Stable Estimation Procedure for the Multivariate Linear Mixed-Effect Model and Application to Malaria Public Health Problem. The International Journal of Biostatistics, 0(0), pp. -. Retrieved 25 Jun. 2019, from doi:10.1515/ijb-2017-0076  (see free downloadable version here and some related codes here)

Adjakossa, E.H., Sadissou, I., Hounkonnou, M.N. and Nuel, G., 2016. Multivariate longitudinal analysis with bivariate correlation test. PloS one 11, e0159649. (Some related R codes here)

Adjakossa, E. and Nuel, G., 2017a. Fixed effects selection in the linear mixed-effects model using adaptive ridge procedure for l0 penalty performance. arXiv preprint  arXiv:1705.01308 (see some related codes here)

Staerman G., Adjakossa E., Mozharovskyi P., Hofer V., Sen Gupta J., Clémençon S., 2022. Functional Anomaly Detection: a Benchmark Study (Int J Data Sci Anal (2022). https://doi.org/10.1007/s41060-022-00366-5) (see some related Python codes here)

Interpretability of anomaly scores: a suggested method (working paper)