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• [6]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984297

  Oelschläger, L., & Bauer, D. (2023). Bayesian probit models for preference classification: an analysis of chess players’ propensity for risk-taking. In TU Dortmund University (Ed.), Proceedings of the 37th International Workshop on Statistical Modelling (pp. 549-553).

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• [5]
  

  2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2957037

  Oelschläger, L., & Adam, T. (2021). Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models. Statistical Modelling, 23(2), 107-126. https://doi.org/10.1177/1471082X211034048

  PUB | DOI | WoS
   
• [4]
  

  2021 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2951073

  Oelschläger, L., & Bauer, D. (2021). Bayes Estimation of Latent Class Mixed Multinomial Probit Models. Presented at the TRB Annual Meeting 2021, Online.

  PUB | PDF
   
• [3]
  

  2021 | Wissenschaftliche Software | PUB-ID: 2982764

  Oelschläger, L., Adam, T., & Michels, R. (2021). fHMM: fitting hidden Markov models to financial data (R package). CRAN.

  PUB
   
• [2]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982766

  Adam, T., & Oelschläger, L. (2020). Hidden Markov models for multi-scale time series: an application to stock market data. In I. Irigoien, D. - J. Lee, J. Martínez-Minaya, & M. X. Rodríguez-Álvarez (Eds.), Proceedings of the 35th International Workshop on Statistical Modelling. Part I (pp. 2-7). Bilbao: Universidad del País Vasco.

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• [1]
  

  2020 | Konferenzbeitrag | PUB-ID: 2951071

  Oelschläger, L., & Bauer, D. (2020). Bayes Estimation of Latent Class Mixed Multinomial Probit Models. Presented at the TRB Annual Meeting 2021.

  PUB