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)
Adjakossa, E.H.,Goude,Y., Wintenberger, O., 2019. Kalman Recursions Aggregated Online (Submitted; 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 (submitted; see some related codes here)
Adjakossa E., Staerman G., Sen Gupta J., Mozharovskyi P., Clémençon S., 2020. Recent Anomaly Detection Techniques for Functional Observations: a Benchmark Study on Aeronautics Data. (submitted; see some related Python codes here)
Interpretability of anomaly scores: a suggested method (working paper)