| Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models L Oelschläger, T Adam Statistical Modelling, 2020 | 11 | 2020 |
| Hidden Markov models for multi-scale time series: an application to stock market data T Adam, L Oelschläger 35th International Workshop on Statistical Modelling, 2, 2020 | 4 | 2020 |
| Bayesian probit models for preference classification L Oelschläger, D Bauer Proceedings of the 37th International Workshop on Statistical Modelling, 2023 | | 2023 |
| ino: Analysis of Initialization for Numerical Optimization L Oelschläger, M Ötting https://CRAN.R-project.org/package=ino, 2023 | | 2023 |
| RprobitB: Bayesian Probit Choice Modeling L Oelschläger, D Bauer https://CRAN.R-project.org/package=RprobitB, 2022 | | 2022 |
| fHMM: fitting hidden Markov models to financial data (R package) L Oelschläger, T Adam, R Michels | | 2021 |
| Bayes Estimation of Latent Class Mixed Multinomial Probit Models L Oelschläger, D Bauer TRB Annual Meeting 2021, 2020 | | 2020 |
| Detection of Bearish and Bullish Markets in the DAX Using Hierarchical Hidden Markov Models L Oelschläger Master thesis, 2019 | | 2019 |
Timo AdamBielefeld UniversityVerified email at uni-bielefeld.de
