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
    R.J. Lennox, et al., “Movement in 3D: Novel Opportunities for Understanding Animal Behaviour and Space Use”, Ethology , 2024.
    PUB | DOI | WoS
     
  • [25]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2990082 OA
    T. Adam, M. Ötting, and R. Michels, “Markov-switching decision trees”, AStA Advances in Statistical Analysis, vol. 108, 2024, pp. 461–476.
    PUB | PDF | DOI | WoS
     
  • [24]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982750
    R. Glennie, et al., “Hidden Markov models: pitfalls and opportunities in ecology”, Methods in Ecology and Evolution, vol. 14, 2023, pp. 43-56.
    PUB | DOI
     
  • [23]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982760
    T. Adam, M. Ötting, and R. Michels, “State-switching decision trees”, Proceedings of the 37th International Workshop on Statistical Modelling. Part II, E. Bergherr, A. Groll, and A. Mayr, eds., Dortmund: TU Dortmund, 2023, pp.321-325.
    PUB
     
  • [22]
    2022 | Blogbeitrag | Veröffentlicht | PUB-ID: 2982761
    T. Adam, “Hidden Markov models have pitfalls…”, Methods Blog: the Latest Methods in Ecology and Evolution, 2022.
    PUB | Download (ext.)
     
  • [21]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982759
    T. Adam, R. Glennie, and T. Michelot, “State-switching varying-coefficient stochastic differential equations”, Proceedings of the 36th International Workshop on Statistical Modelling. Part II, N. Torelli, R. Bellio, and V. Muggeo, eds., Edizioni Università di Trieste, 2022, pp.53-57.
    PUB
     
  • [20]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961460
    R. Nathan, et al., “Big-data approaches lead to an increased understanding of the ecology of animal movement”, Science, vol. 375, 2022, : eabg1780.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [19]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2963807
    J.M. Pohle, T. Adam, and L.T. Beumer, “Flexible estimation of the state dwell-time distribution in hidden semi-Markov models”, Computational Statistics & Data Analysis , vol. 172, 2022, : 107479.
    PUB | DOI | WoS
     
  • [18]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2962700
    T. Adam, A. Mayr, and T. Kneib, “Gradient boosting in Markov-switching generalized additive models for location, scale, and shape”, Econometrics and Statistics , vol. 22, 2022, pp. 3-16.
    PUB | DOI | WoS
     
  • [17]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2955071 OA
    R. Nagel, et al., “Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups”, Scientific reports, vol. 11, 2021, : 14323.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC | Preprint
     
  • [16]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982762
    M. Aquino‐Baleytó, et al., “Diving deeper into the underlying white shark behaviors at Guadalupe Island, Mexico”, Ecology and Evolution, vol. 11, 2021, pp. 14932-14949.
    PUB | DOI
     
  • [15]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982757
    R.J. Lennox, et al., “A role for lakes in revealing the nature of animal movement using high dimensional telemetry systems”, Movement Ecology, vol. 9, 2021, : 40.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [14]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2957037
    L. Oelschläger and T. Adam, “Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models”, Statistical Modelling, vol. 23, 2021, pp. 107-126.
    PUB | DOI | WoS
     
  • [13]
    2021 | Wissenschaftliche Software | PUB-ID: 2982764
    L. Oelschläger, T. Adam, and R. Michels, fHMM: fitting hidden Markov models to financial data (R package), CRAN, 2021.
    PUB
     
  • [12]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982766
    T. Adam and L. Oelschläger, “Hidden Markov models for multi-scale time series: an application to stock market data”, Proceedings of the 35th International Workshop on Statistical Modelling. Part I, I. Irigoien, et al., eds., Bilbao: Universidad del País Vasco, 2020, pp.2-7.
    PUB
     
  • [11]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982763
    J.M. Pohle, et al., “Flexible estimation of the state dwell-time distribution in hidden semi-Markov models”, Proceedings of the 35th International Workshop on Statistical Modelling. Part I, I. Irigoien, et al., eds., Bilbao: Universidad del País Vasco, 2020, pp.189-193.
    PUB
     
  • [10]
    2020 | Bielefelder E-Dissertation | PUB-ID: 2949182 OA
    T. Adam, On some flexible extensions of hidden Markov models, Bielefeld: Universität Bielefeld, 2020.
    PUB | PDF | DOI
     
  • [9]
    2019 | Wissenschaftliche Software | PUB-ID: 2982765
    T. Adam, countHMM: penalized estimation of flexible hidden Markov models for time series of counts (R package), CRAN, 2019.
    PUB
     
  • [8]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941675
    T. Adam, R. Langrock, and C. Weiß, “Non-parametric inference in hidden Markov models for time series of counts”, Proceedings of the 34th International Workshop on Statistical Modelling, Volume I, 2019, pp.135-140.
    PUB
     
  • [7]
    2019 | Konferenzbeitrag | PUB-ID: 2941674
    T. Adam, R. Langrock, and T. Kneib, “Model-based clustering of time series data: a flexible approach using non-parametric state-switching quantile regression models”, Proceedings of the 12th Scientific Meeting on Classification and Data Analysis, 2019, pp.19-22.
    PUB
     
  • [6]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2937689
    T. Adam, et al., “Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models”, Methods in Ecology and Evolution, vol. 10, 2019, pp. 1536-1550.
    PUB | DOI | WoS
     
  • [5]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2936764
    T. Adam, R. Langrock, and C.H. Weiß, “Penalized estimation of flexible hidden Markov models for time series of counts”, Metron, vol. 77, 2019, pp. 87-104.
    PUB | DOI | WoS
     
  • [4]
    2018 | Konferenzbeitrag | PUB-ID: 2933002
    T. Adam, et al., “Statistical boosting for Markov-switching distributional regression models”, Proceedings of the 33rd International Workshop on Statistical Modelling, Vol. 1, 2018, pp.30-35.
    PUB
     
  • [3]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2916816
    R. Langrock, et al., “Spline-based nonparametric inference in general state-switching models”, Statistica Neerlandica, vol. 72, 2018, pp. 179-200.
    PUB | DOI | Download (ext.) | WoS
     
  • [2]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911528
    V. Leos-Barajas, et al., “Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures”, Journal of Agricultural, Biological and Environmental Statistics, vol. 22, 2017, pp. 232–248.
    PUB | DOI | WoS
     
  • [1]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913894
    T. Adam, et al., “Using hierarchical hidden Markov models for joint inference at multiple temporal scales”, Proceedings of the 32nd International Workshop on Statistical Modelling, Vol. 2, Groningen, Netherlands 3-7 July, 2017, M. Grzegorczyk and G. Ceoldo, eds., Groningen: Univ. of Groningen, 2017.
    PUB
     
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