Timo Adam

timo.adam@uni-bielefeld.de
PEVZ-ID
78829300

26 Publikationen

Alle markieren

  • [26]
    2024 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2999960
    Lennox, R.J., et al., 2024. Movement in 3D: Novel Opportunities for Understanding Animal Behaviour and Space Use. Ethology .
    PUB | DOI | WoS
     
  • [25]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2990082 OA
    Adam, T., Ötting, M., & Michels, R., 2024. Markov-switching decision trees. AStA Advances in Statistical Analysis, 108(2), p 461–476.
    PUB | PDF | DOI | WoS
     
  • [24]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982750
    Glennie, R., et al., 2023. Hidden Markov models: pitfalls and opportunities in ecology. Methods in Ecology and Evolution, 14(1), p 43-56.
    PUB | DOI
     
  • [23]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982760
    Adam, T., Ötting, M., & Michels, R., 2023. State-switching decision trees. In E. Bergherr, A. Groll, & A. Mayr, eds. Proceedings of the 37th International Workshop on Statistical Modelling. Part II. Dortmund: TU Dortmund, pp. 321-325.
    PUB
     
  • [22]
    2022 | Blogbeitrag | Veröffentlicht | PUB-ID: 2982761
    Adam, T., 2022. Hidden Markov models have pitfalls…. Methods Blog: the Latest Methods in Ecology and Evolution.
    PUB | Download (ext.)
     
  • [21]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982759
    Adam, T., Glennie, R., & Michelot, T., 2022. State-switching varying-coefficient stochastic differential equations. In N. Torelli, R. Bellio, & V. Muggeo, eds. Proceedings of the 36th International Workshop on Statistical Modelling. Part II. Edizioni Università di Trieste, pp. 53-57.
    PUB
     
  • [20]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961460
    Nathan, R., et al., 2022. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science, 375(6582): eabg1780.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [19]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2963807
    Pohle, J.M., Adam, T., & Beumer, L.T., 2022. Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. Computational Statistics & Data Analysis , 172: 107479.
    PUB | DOI | WoS
     
  • [18]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2962700
    Adam, T., Mayr, A., & Kneib, T., 2022. Gradient boosting in Markov-switching generalized additive models for location, scale, and shape. Econometrics and Statistics , 22, p 3-16.
    PUB | DOI | WoS
     
  • [17]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2955071 OA
    Nagel, R., et al., 2021. Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups. Scientific reports, 11(1): 14323.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC | Preprint
     
  • [16]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982762
    Aquino‐Baleytó, M., et al., 2021. Diving deeper into the underlying white shark behaviors at Guadalupe Island, Mexico. Ecology and Evolution, 11(21), p 14932-14949.
    PUB | DOI
     
  • [15]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982757
    Lennox, R.J., et al., 2021. A role for lakes in revealing the nature of animal movement using high dimensional telemetry systems. Movement Ecology, 9(1): 40.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [14]
    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), p 107-126.
    PUB | DOI | WoS
     
  • [13]
    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
     
  • [12]
    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, et al., eds. Proceedings of the 35th International Workshop on Statistical Modelling. Part I. Bilbao: Universidad del País Vasco, pp. 2-7.
    PUB
     
  • [11]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982763
    Pohle, J.M., et al., 2020. Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. In I. Irigoien, et al., eds. Proceedings of the 35th International Workshop on Statistical Modelling. Part I. Bilbao: Universidad del País Vasco, pp. 189-193.
    PUB
     
  • [10]
    2020 | Bielefelder E-Dissertation | PUB-ID: 2949182 OA
    Adam, T., 2020. On some flexible extensions of hidden Markov models, Bielefeld: Universität Bielefeld.
    PUB | PDF | DOI
     
  • [9]
    2019 | Wissenschaftliche Software | PUB-ID: 2982765
    Adam, T., 2019. countHMM: penalized estimation of flexible hidden Markov models for time series of counts (R package), CRAN.
    PUB
     
  • [8]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941675
    Adam, T., Langrock, R., & Weiß, C., 2019. Non-parametric inference in hidden Markov models for time series of counts. In Proceedings of the 34th International Workshop on Statistical Modelling, Volume I. pp. 135-140.
    PUB
     
  • [7]
    2019 | Konferenzbeitrag | PUB-ID: 2941674
    Adam, T., Langrock, R., & Kneib, T., 2019. Model-based clustering of time series data: a flexible approach using non-parametric state-switching quantile regression models. In Proceedings of the 12th Scientific Meeting on Classification and Data Analysis. pp. 19-22.
    PUB
     
  • [6]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2937689
    Adam, T., et al., 2019. Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models. Methods in Ecology and Evolution, 10(9), p 1536-1550.
    PUB | DOI | WoS
     
  • [5]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2936764
    Adam, T., Langrock, R., & Weiß, C.H., 2019. Penalized estimation of flexible hidden Markov models for time series of counts. Metron, 77(2), p 87-104.
    PUB | DOI | WoS
     
  • [4]
    2018 | Konferenzbeitrag | PUB-ID: 2933002
    Adam, T., et al., 2018. Statistical boosting for Markov-switching distributional regression models. In Proceedings of the 33rd International Workshop on Statistical Modelling, Vol. 1. pp. 30-35.
    PUB
     
  • [3]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2916816
    Langrock, R., et al., 2018. Spline-based nonparametric inference in general state-switching models. Statistica Neerlandica, 72(3), p 179-200.
    PUB | DOI | Download (ext.) | WoS
     
  • [2]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911528
    Leos-Barajas, V., et al., 2017. Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures. Journal of Agricultural, Biological and Environmental Statistics, 22(3), p 232–248.
    PUB | DOI | WoS
     
  • [1]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913894
    Adam, T., et al., 2017. Using hierarchical hidden Markov models for joint inference at multiple temporal scales. In M. Grzegorczyk & G. Ceoldo, eds. Proceedings of the 32nd International Workshop on Statistical Modelling, Vol. 2, Groningen, Netherlands 3-7 July, 2017. Groningen: Univ. of Groningen.
    PUB
     
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