Prof. Dr. Roland Langrock
- BIGSEM - Bielefeld Graduate School of Economics and Management
- Bielefeld Center for Data Science (BiCDaS)
- Bielefeld Graduate School in Theoretical Sciences
- Center for Uncertainty Studies (CeUS)
- Department Empirische Methoden
- Fakultät für Wirtschaftswissenschaften
- Institut für Technologische Innovation, Marktentwicklung und Entrepreneurship
- JICE - Joint Institute for Individualisation in a Changing Environment
- Lehrstuhl für Statistik und Datenanalyse
- SFB/Transregio 212 "A Novel Synthesis of Individualisation across Behaviour, Ecology and Evolution: Niche Choice, Niche Conformance, Niche Construction" (NC³)
- Zentrum für Statistik
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2025; TRR 212/2, Teilprojekt D06: Aufdeckung komplexer Mechanismen bezüglich Verhaltensnischen anhand von ökologischen Zeitreihen auf der Ebene des Individuums; Langrock, Roland; Deutsche Forschungsgemeinschaft
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2023; Datengestützte Indikation von Betrug bei Live-Wetten, 1. Förderphase; Deutscher, Christian und Langrock, Roland; Deutsche Forschungsgemeinschaft
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2021; TRR 212/1, Teilprojekt D06: Aufdeckung komplexer Mechanismen bezüglich Verhaltensnischen anhand von ökologischen Zeitreihen auf der Ebene des Individuums; Langrock, Roland; Deutsche Forschungsgemeinschaft
Projektmitglied
- Ötting M, Michels R, Langrock R, Deutscher C. Demand for live betting: An analysis using state‐space models. Applied Stochastic Models in Business and Industry. 2024.
- Stoye F, Langrock R. Autoregressive hidden Markov models for high-resolution animal movement data. In: Bergherr E, Groll A, Mayr A, eds. Proceedings of the 37th International Workshopon Statistical Modelling. Part II. 2023: 294-299.
- Michels R, Ötting M, Langrock R. Bettors’ reaction to match dynamics: Evidence from in-game betting. European Journal of Operational Research. In Press.
- Feldmann C, Mews S, Coculla A, Stanewsky R, Langrock R. Flexible Modelling of Diel and Other Periodic Variation in Hidden Markov Models. Journal of Statistical Theory and Practice . 2023;17(3): 45.
- Nathan R, Monk CT, Arlinghaus R, et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science. 2022;375(6582): eabg1780.
- Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Classification-based model selection in retail demand forecasting. International Journal of Forecasting . 2022;38(1):209-223.
- Winkelmann D, Ulrich M, Römer M, Langrock R, Jahnke H. Dynamic Stochastic Inventory Management in E-Grocery Retailing: The Value of Probabilistic Information. arXiv:2205.06572. 2022.
- Kaiser MI, Malsch A, Abendroth A, et al. Interdisciplinary perspectives on individualisation in environments. Interdisciplinary Journal of Philosophy. Unpublished.
- Mews S, Langrock R, Ötting M, Yaqine H, Reinecke J. Maximum approximate likelihood estimation of general continuous-time state-space models. Statistical Modelling. 2022: 1471082X211065785.
- Mews S, Langrock R, King R, Quick N. Multistate capture–recapture models for irregularly sampled data. Annals of Applied Statistics. 2022;16(2):982-998.
- Lieber L, Füchtencordsjürgen C, Hilder RL, et al. Selective foraging behavior of seabirds in small-scale slicks. Limnology and Oceanography Letters . 2022.
- Lieber L, Langrock R, Nimmo-Smith WAM. A bird's-eye view on turbulence: seabird foraging associations with evolving surface flow features. Proceedings of the Royal Society B: Biological Sciences. 2021;288(1949): 20210592.
- Ötting M, Langrock R, Maruotti A. A copula-based multivariate hidden Markov model for modelling momentum in football. AStA Advances in Statistical Analysis. 2021.
- Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery. European Journal of Operational Research. 2021;294(3):831-842.
- Byrnes EE, Daly R, Leos-Barajas V, Langrock R, Gleiss AC. Evaluating the constraints governing activity patterns of a coastal marine top predator. Marine Biology. 2021;168(1): 11.
- Krüger O, Anaya-Rojas J, Caspers B, et al. Individualised niches: an integrative conceptual framework across behaviour, ecology, and evolution. EcoEvoRxiv. 2021.
- Schwarz J, Mews S, De Rango E, et al. Individuality counts: A new comprehensive approach to foraging strategies of a tropical marine predator. Oecologia. 2021;195(2):313–325.
- Nagel R, Mews S, Adam T, et al. Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups. Scientific reports. 2021;11(1): 14323.
- Deutscher C, Michels R, Ötting M, Langrock R. Patterns of betting behavior for news in live betting markets. In: Deutscher C, Wicker P, eds. 12th ESEA Conference on Sport Economics – Book of Abstracts. 2021: 33-35.
- Williams R, Ashe E, Yruretagoyena L, et al. Reducing vessel noise increases foraging in endangered killer whales. Marine Pollution Bulletin. 2021;173(Pt A): 112976.
- Beumer LT, Pohle JM, Schmidt NM, et al. An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore. Movement Ecology. 2020;8(1): 25.
- Pohle JM, Langrock R, Schaar M van der, King R, Jensen FH. A primer on coupled state-switching models for multiple interacting time series. Statistical Modelling. 2020: 1471082X2095642.
- Mews S, Langrock R, King R, Quick N. Continuous-time multi-state capture-recapture models. arXiv:2002.10997. 2020.
- Pohle JM, Adam T, Langrock R, Beumer L. Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. In: Irigoien I, Lee D-J, Martínez-Minaya J, Rodríguez-Álvarez MX, eds. Proceedings of the 35th International Workshop on Statistical Modelling. Part I. Bilbao: Universidad del País Vasco; 2020: 189-193.
- Glennie R, Buckland ST, Langrock R, et al. Incorporating animal movement into distance sampling. Journal of the American Statistical Association. 2020:1-17.
- Mews S, Langrock R, Ötting M, Yaqine H, Reinecke J. Maximum approximate likelihood estimation of general continuous-time state-space models. arXiv:2010.14883. 2020.
- Ötting M, Deutscher C, Schneemann S, Langrock R, Gehrmann S, Scholten H. Performance under pressure in skill tasks: An analysis of professional darts. PLOS ONE. 2020;15(2): e0228870.
- Harrington KJ, Fahlbusch JA, Langrock R, Therrien J-F, Houtz JL, McDonald BI. Seasonal activity levels of a farm-island population of striated caracaras (Phalcoboenus australis) in the Falkland Islands. Animal Biotelemetry. 2020;8(1): 27.
- Ötting M, Langrock R, Deutscher C, Leos-Barajas V. The hot hand in professional darts. Journal of the Royal Statistical Society. Series A. 2020;183(2):565-580.
- McClintock BT, Langrock R, Gimenez O, et al. Uncovering ecological state dynamics with hidden Markov models. Ecology Letters. 2020: ele.13610.
- Mews S, Langrock R, King R, Schemm J, Janzen I, Quick N. A continuous-time capture-recapture model for annual movements of bottlenose dolphins. In: Proceedings of the 34th International Workshop on Statistical Modelling. Volume I. 2019: 169-174.
- Ötting M, Langrock R, Maruotti A. A copula-based multivariate hidden Markov model for modelling momentum in football. In: Proceedings of the 34th International Workshop on Statistical Modelling. Volume I. 2019: 125-129.
- van Beest FM, Mews S, Elkenkamp S, et al. Classifying grey seal behaviour in relation to environmental variability and commercial fishing activity. A multivariate hidden Markov model. Scientific Reports. 2019;9(1): 5642.
- Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery. Universität Bielefeld Working Papers in Economics and Management. Vol 09-2018. Bielefeld: Bielefeld University, Department of Business Administration and Economics; 2019.
- Bulla J, Langrock R, Maruotti A. Guest editor’s introduction to the special issue on “Hidden Markov Models: Theory and Applications”. Metron. 2019;77(2):63-66.
- Adam T, Griffiths CA, Leos-Barajas V, et al. Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models. Methods in Ecology and Evolution. 2019;10(9):1536-1550.
- Adam T, Langrock R, Kneib T. 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. 2019: 19-22.
- Pohle JM, Ötting M, Jensen FH, Langrock R. Modeling interactions between individuals using coupled hidden Markov models. In: Proceedings of the 34th International Workshop on Statistical Modelling. Volume I. 2019: 57-61.
- Adam T, Langrock R, Weiß C. Non-parametric inference in hidden Markov models for time series of counts. In: Proceedings of the 34th International Workshop on Statistical Modelling, Volume I. 2019: 135-140.
- Adam T, Langrock R, Weiß CH. Penalized estimation of flexible hidden Markov models for time series of counts. Metron. 2019;77(2):87-104.
- Papastamatiou YP, Watanabe YY, Demšar U, et al. Activity seascapes highlight central place foraging strategies in marine predators that never stop swimming. Movement Ecology. 2018;6(1): 9.
- Hambuckers J, Kneib T, Langrock R, Sohn A. A Markov-switching generalized additive model for compound Poisson processes, with applications to operational losses models. Quantitative Finance. 2018;18(10):1679-1698.
- Pohle JM, King R, van der Schaar M, Langrock R. Coupled Markov-switching regression: inference and a case study using electronic health record data. In: Proceedings of the 33rd International Workshop on Statistical Modelling, Vol. 1. 2018: 242-246.
- Langrock R, Ulrich M, Jahnke H, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery — a case study. In: Proceedings of the 33rd International Workshop on Statistical Modelling. Volume 2. 2018: 171-176.
- Ötting M, Langrock R, Deutscher C. Integrating multiple data sources in match-fixing warning systems. Statistical Modelling. 2018;18(5-6):483-504.
- Langrock R, Adam T, Leos-Barajas V, Mews S, Miller DL, Papastamatiou YP. Spline-based nonparametric inference in general state-switching models. Statistica Neerlandica. 2018;72(3):179-200.
- Adam T, Mayr A, Kneib T, Langrock R. Statistical boosting for Markov-switching distributional regression models. In: Proceedings of the 33rd International Workshop on Statistical Modelling, Vol. 1. 2018: 30-35.
- Ötting M, Langrock R, Deutscher C, Leos-Barajas V. The hot hand in professional darts. In: Proceedings of the 33rd International Workshop on Statistical Modelling, Vol. 1. 2018: 231-236.
- DeRuiter SL, Langrock R, Skirbutas T, et al. A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure. The Annals of Applied Statistics. 2017;11(1):362-392.
- Leos-Barajas V, Photopoulou T, Langrock R, et al. Analysis of animal accelerometer data using hidden Markov models. Methods in Ecology and Evolution. 2017;8(2):161-173.
- Popov V, Langrock R, DeRuiter SL, Visser F. An analysis of pilot whale vocalization activity using hidden Markov models. The Journal of the Acoustical Society of America. 2017;141(1):159-171.
- Ötting M, Deutscher C, Langrock R. Detecting Match-Fixing in the Italian Serie B Using Flexible Regression. In: Grzegorczyk, M, Ceoldo G, eds. Proceedings of the 32nd International Workshop on Statistical Modelling. Volume II. 2017: 243-246.
- Michelot T, Langrock R, Bestley S, Jonsen ID, Photopoulou T, Patterson TA. Estimation and simulation of foraging trips in land-based marine predators. Ecology. 2017;98(7):1932-1944.
- Langrock R, Borchers DL. Guest editors’ introduction to the special issue on “Ecological Statistics”. AStA Advances in Statistical Analysis. 2017;101(4):345-347.
- Hooten MB, King R, Langrock R. Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”. Journal of Agricultural, Biological and Environmental Statistics. 2017;22(3):224-231.
- Langrock R, Kneib T, Glennie R, Michelot T. Markov-switching generalized additive models. Statistics and Computing. 2017;27(1):259-270.
- Abanto-Valle CA, Langrock R, Chen M-H, Cardoso MV. Maximum likelihood estimation for stochastic volatility in mean models with heavy-tailed distributions. Applied Stochastic Models in Business and Industry. 2017;33(4):394–408.
- Leos-Barajas V, Gangloff EJ, Adam T, 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. 2017;22(3):232–248.
- Pohle JM, Langrock R. Pragmatic order selection in hidden Markov models. In: Proceedings of the 32nd IWSM Vol. 1. 2017.
- Pohle J, Langrock R, van Beest FM, Schmidt NM. Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement. Journal of Agricultural, Biological and Environmental Statistics. 2017;22(3):270–293.
- Patterson TA, Parton A, Langrock R, Blackwell PG, Thomas L, King R. Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges. AStA Advances in Statistical Analysis. 2017;101(4):399-438.
- Adam T, Leos-Barajas V, Langrock R, van Beest F. Using hierarchical hidden Markov models for joint inference at multiple temporal scales. In: Grzegorczyk M, Ceoldo G, eds. Proceedings of the 32nd International Workshop on Statistical Modelling, Vol. 2, Groningen, Netherlands 3-7 July, 2017. Groningen: Univ. of Groningen; 2017.
- Zucchini W, MacDonald IL, Langrock R. Hidden Markov Models for Time Series: An Introduction Using R. Monographs on statistics and applied probability. Vol 150 2nd ed. Boca Raton: CRC Press, Taylor & Francis; 2016.
- Michelot T, Langrock R, Patterson TA. moveHMM: An R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol Evol. 2016;7(11):1308-1315.
- Towner AV, Leos-Barajas V, Langrock R, et al. Sex-specific and individual preferences for hunting strategies in white sharks. Functional Ecology. 2016;30(8):1397-1407.
- Borchers DL, Langrock R. Double-observer line transect surveys with Markov-modulated Poisson process models for animal availability. Biometrics. 2015;71(4):1060-1069.
- Michelot T, Langrock R, Kneib T, King R. Maximum penalized likelihood estimation in semiparametric mark-recapture-recovery models. Biometrical Journal. 2015;58(1):222-239.
- Langrock R, Kneib T, Sohn A, DeRuiter SL. Nonparametric inference in hidden Markov models using P-splines. Biometrics. 2015;71(2):520-528.
- King R, Langrock R. Semi-Markov Arnason-Schwarz models. Biometrics. 2015;72(2):619-628.
- Langrock R, Michelot T, Sohn A, Kneib T. Semiparametric stochastic volatility modelling using penalized splines. Computational Statistics. 2015;30(2):517-537.
- Langrock R, Heidenreich N-B, Sperlich S. Kernel-based semiparametric multinomial logit modelling of political party preferences. Statistical Methods & Applications. 2014;23(3):435-449.
- Langrock R, Hopcraft JGC, Blackwell PG, et al. Modelling group dynamic animal movement. Methods in Ecology and Evolution. 2014;5(2):190-199.
- McKellar AE, Langrock R, Walters JR, Kesler DC. Using mixed hidden Markov models to examine behavioral states in a cooperatively breeding bird. Behavioral Ecology. 2014;26(1):148-157.
- Langrock R, Swihart BJ, Caffo BS, Punjabi NM, Crainiceanu CM. Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms. Statistics in Medicine. 2013;32(19):3342-3356.
- Langrock R, Borchers DL, Skaug HJ. Markov-modulated nonhomogeneous Poisson processes for modeling detections in surveys of marine mammal abundance. Journal of the American Statistical Association. 2013;108(503):840-851.
- Langrock R, King R. Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates. Ann. Appl. Stat. 2013;7(3):1709-1732.
- Langrock R, Marques TA, Baird RW, Thomas L. Modeling the Diving Behavior of Whales: A Latent-Variable Approach with Feedback and Semi-Markovian Components. Journal of Agricultural, Biological, and Environmental Statistics. 2013;19(1):82-100.
- Borchers DL, Zucchini W, Heide-Jørgensen MP, Cañadas A, Langrock R. Using hidden Markov models to deal with availability bias on line transect surveys. Biometrics. 2013;69(3):703-713.
- Langrock R, King R, Matthiopoulos J, Thomas L, Fortin D, Morales JM. Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology. 2012;93(11):2336-2342.
- Langrock R. Flexible Latent-State Modelling of Old Faithful's Eruption Inter-Arrival Times in 2009. Australian & New Zealand Journal of Statistics. 2012;54(3):261-279.
- Wichelhaus C, Langrock R. Nonparametric inference for stochastic feedforward networks based on cross-spectral analysis of point processes. Electronic Journal of Statistics. 2012;6:1670-1714.
- Schliehe-Diecks S, Kappeler PM, Langrock R. On the application of mixed hidden Markov models to multiple behavioural time series. Interface Focus. 2012;2(2):180-189.
- Langrock R, Zucchini W. Hidden Markov models with arbitrary state dwell-time distributions. Computational Statistics & Data Analysis. 2011;55(1):715-724.
- Langrock R. Some applications of nonlinear and non-Gaussian state–space modelling by means of hidden Markov models. Journal of Applied Statistics. 2011;38(12):2955-2970.
- Langrock R, MacDonald IL, Zucchini W. Some nonstandard stochastic volatility models and their estimation using structured hidden Markov models. Journal of Empirical Finance. 2011;19(1):147-161.
Publikationsliste
Publikationsliste (CSV)
Publikationsliste (PDF) im "AMA"-Zitierstil
Publikationsliste (PDF) im "APA (6th ed.)"-Zitierstil
Publikationsliste (PDF) im "Chicago"-Zitierstil
Publikationsliste (PDF) im "Frontiers"-Zitierstil
Publikationsliste (PDF) im "Harvard"-Zitierstil
Publikationsliste (PDF) im "IEEE"-Zitierstil
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Publikationsliste (PDF) im "MLA"-Zitierstil
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2025; TRR 212/2, Teilprojekt D06: Aufdeckung komplexer Mechanismen bezüglich Verhaltensnischen anhand von ökologischen Zeitreihen auf der Ebene des Individuums; Langrock, Roland; Deutsche Forschungsgemeinschaft
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2023; Datengestützte Indikation von Betrug bei Live-Wetten, 1. Förderphase; Deutscher, Christian und Langrock, Roland; Deutsche Forschungsgemeinschaft
-
2021; TRR 212/1, Teilprojekt D06: Aufdeckung komplexer Mechanismen bezüglich Verhaltensnischen anhand von ökologischen Zeitreihen auf der Ebene des Individuums; Langrock, Roland; Deutsche Forschungsgemeinschaft
Als wiss. Mitarbeiter*in
- Ötting M, Michels R, Langrock R, Deutscher C. Demand for live betting: An analysis using state‐space models. Applied Stochastic Models in Business and Industry. 2024.
- Stoye F, Langrock R. Autoregressive hidden Markov models for high-resolution animal movement data. In: Bergherr E, Groll A, Mayr A, eds. Proceedings of the 37th International Workshopon Statistical Modelling. Part II. 2023: 294-299.
- Michels R, Ötting M, Langrock R. Bettors’ reaction to match dynamics: Evidence from in-game betting. European Journal of Operational Research. In Press.
- Feldmann C, Mews S, Coculla A, Stanewsky R, Langrock R. Flexible Modelling of Diel and Other Periodic Variation in Hidden Markov Models. Journal of Statistical Theory and Practice . 2023;17(3): 45.
- Nathan R, Monk CT, Arlinghaus R, et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science. 2022;375(6582): eabg1780.
- Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Classification-based model selection in retail demand forecasting. International Journal of Forecasting . 2022;38(1):209-223.
- Winkelmann D, Ulrich M, Römer M, Langrock R, Jahnke H. Dynamic Stochastic Inventory Management in E-Grocery Retailing: The Value of Probabilistic Information. arXiv:2205.06572. 2022.
- Kaiser MI, Malsch A, Abendroth A, et al. Interdisciplinary perspectives on individualisation in environments. Interdisciplinary Journal of Philosophy. Unpublished.
- Mews S, Langrock R, Ötting M, Yaqine H, Reinecke J. Maximum approximate likelihood estimation of general continuous-time state-space models. Statistical Modelling. 2022: 1471082X211065785.
- Mews S, Langrock R, King R, Quick N. Multistate capture–recapture models for irregularly sampled data. Annals of Applied Statistics. 2022;16(2):982-998.
- Lieber L, Füchtencordsjürgen C, Hilder RL, et al. Selective foraging behavior of seabirds in small-scale slicks. Limnology and Oceanography Letters . 2022.
- Lieber L, Langrock R, Nimmo-Smith WAM. A bird's-eye view on turbulence: seabird foraging associations with evolving surface flow features. Proceedings of the Royal Society B: Biological Sciences. 2021;288(1949): 20210592.
- Ötting M, Langrock R, Maruotti A. A copula-based multivariate hidden Markov model for modelling momentum in football. AStA Advances in Statistical Analysis. 2021.
- Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery. European Journal of Operational Research. 2021;294(3):831-842.
- Byrnes EE, Daly R, Leos-Barajas V, Langrock R, Gleiss AC. Evaluating the constraints governing activity patterns of a coastal marine top predator. Marine Biology. 2021;168(1): 11.
- Krüger O, Anaya-Rojas J, Caspers B, et al. Individualised niches: an integrative conceptual framework across behaviour, ecology, and evolution. EcoEvoRxiv. 2021.
- Schwarz J, Mews S, De Rango E, et al. Individuality counts: A new comprehensive approach to foraging strategies of a tropical marine predator. Oecologia. 2021;195(2):313–325.
- Nagel R, Mews S, Adam T, et al. Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups. Scientific reports. 2021;11(1): 14323.
- Deutscher C, Michels R, Ötting M, Langrock R. Patterns of betting behavior for news in live betting markets. In: Deutscher C, Wicker P, eds. 12th ESEA Conference on Sport Economics – Book of Abstracts. 2021: 33-35.
- Williams R, Ashe E, Yruretagoyena L, et al. Reducing vessel noise increases foraging in endangered killer whales. Marine Pollution Bulletin. 2021;173(Pt A): 112976.
- Beumer LT, Pohle JM, Schmidt NM, et al. An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore. Movement Ecology. 2020;8(1): 25.
- Pohle JM, Langrock R, Schaar M van der, King R, Jensen FH. A primer on coupled state-switching models for multiple interacting time series. Statistical Modelling. 2020: 1471082X2095642.
- Mews S, Langrock R, King R, Quick N. Continuous-time multi-state capture-recapture models. arXiv:2002.10997. 2020.
- Pohle JM, Adam T, Langrock R, Beumer L. Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. In: Irigoien I, Lee D-J, Martínez-Minaya J, Rodríguez-Álvarez MX, eds. Proceedings of the 35th International Workshop on Statistical Modelling. Part I. Bilbao: Universidad del País Vasco; 2020: 189-193.
- Glennie R, Buckland ST, Langrock R, et al. Incorporating animal movement into distance sampling. Journal of the American Statistical Association. 2020:1-17.
- Mews S, Langrock R, Ötting M, Yaqine H, Reinecke J. Maximum approximate likelihood estimation of general continuous-time state-space models. arXiv:2010.14883. 2020.
- Ötting M, Deutscher C, Schneemann S, Langrock R, Gehrmann S, Scholten H. Performance under pressure in skill tasks: An analysis of professional darts. PLOS ONE. 2020;15(2): e0228870.
- Harrington KJ, Fahlbusch JA, Langrock R, Therrien J-F, Houtz JL, McDonald BI. Seasonal activity levels of a farm-island population of striated caracaras (Phalcoboenus australis) in the Falkland Islands. Animal Biotelemetry. 2020;8(1): 27.
- Ötting M, Langrock R, Deutscher C, Leos-Barajas V. The hot hand in professional darts. Journal of the Royal Statistical Society. Series A. 2020;183(2):565-580.
- McClintock BT, Langrock R, Gimenez O, et al. Uncovering ecological state dynamics with hidden Markov models. Ecology Letters. 2020: ele.13610.
- Mews S, Langrock R, King R, Schemm J, Janzen I, Quick N. A continuous-time capture-recapture model for annual movements of bottlenose dolphins. In: Proceedings of the 34th International Workshop on Statistical Modelling. Volume I. 2019: 169-174.
- Ötting M, Langrock R, Maruotti A. A copula-based multivariate hidden Markov model for modelling momentum in football. In: Proceedings of the 34th International Workshop on Statistical Modelling. Volume I. 2019: 125-129.
- van Beest FM, Mews S, Elkenkamp S, et al. Classifying grey seal behaviour in relation to environmental variability and commercial fishing activity. A multivariate hidden Markov model. Scientific Reports. 2019;9(1): 5642.
- Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery. Universität Bielefeld Working Papers in Economics and Management. Vol 09-2018. Bielefeld: Bielefeld University, Department of Business Administration and Economics; 2019.
- Bulla J, Langrock R, Maruotti A. Guest editor’s introduction to the special issue on “Hidden Markov Models: Theory and Applications”. Metron. 2019;77(2):63-66.
- Adam T, Griffiths CA, Leos-Barajas V, et al. Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models. Methods in Ecology and Evolution. 2019;10(9):1536-1550.
- Adam T, Langrock R, Kneib T. 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. 2019: 19-22.
- Pohle JM, Ötting M, Jensen FH, Langrock R. Modeling interactions between individuals using coupled hidden Markov models. In: Proceedings of the 34th International Workshop on Statistical Modelling. Volume I. 2019: 57-61.
- Adam T, Langrock R, Weiß C. Non-parametric inference in hidden Markov models for time series of counts. In: Proceedings of the 34th International Workshop on Statistical Modelling, Volume I. 2019: 135-140.
- Adam T, Langrock R, Weiß CH. Penalized estimation of flexible hidden Markov models for time series of counts. Metron. 2019;77(2):87-104.
- Papastamatiou YP, Watanabe YY, Demšar U, et al. Activity seascapes highlight central place foraging strategies in marine predators that never stop swimming. Movement Ecology. 2018;6(1): 9.
- Hambuckers J, Kneib T, Langrock R, Sohn A. A Markov-switching generalized additive model for compound Poisson processes, with applications to operational losses models. Quantitative Finance. 2018;18(10):1679-1698.
- Pohle JM, King R, van der Schaar M, Langrock R. Coupled Markov-switching regression: inference and a case study using electronic health record data. In: Proceedings of the 33rd International Workshop on Statistical Modelling, Vol. 1. 2018: 242-246.
- Langrock R, Ulrich M, Jahnke H, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery — a case study. In: Proceedings of the 33rd International Workshop on Statistical Modelling. Volume 2. 2018: 171-176.
- Ötting M, Langrock R, Deutscher C. Integrating multiple data sources in match-fixing warning systems. Statistical Modelling. 2018;18(5-6):483-504.
- Langrock R, Adam T, Leos-Barajas V, Mews S, Miller DL, Papastamatiou YP. Spline-based nonparametric inference in general state-switching models. Statistica Neerlandica. 2018;72(3):179-200.
- Adam T, Mayr A, Kneib T, Langrock R. Statistical boosting for Markov-switching distributional regression models. In: Proceedings of the 33rd International Workshop on Statistical Modelling, Vol. 1. 2018: 30-35.
- Ötting M, Langrock R, Deutscher C, Leos-Barajas V. The hot hand in professional darts. In: Proceedings of the 33rd International Workshop on Statistical Modelling, Vol. 1. 2018: 231-236.
- DeRuiter SL, Langrock R, Skirbutas T, et al. A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure. The Annals of Applied Statistics. 2017;11(1):362-392.
- Leos-Barajas V, Photopoulou T, Langrock R, et al. Analysis of animal accelerometer data using hidden Markov models. Methods in Ecology and Evolution. 2017;8(2):161-173.
- Popov V, Langrock R, DeRuiter SL, Visser F. An analysis of pilot whale vocalization activity using hidden Markov models. The Journal of the Acoustical Society of America. 2017;141(1):159-171.
- Ötting M, Deutscher C, Langrock R. Detecting Match-Fixing in the Italian Serie B Using Flexible Regression. In: Grzegorczyk, M, Ceoldo G, eds. Proceedings of the 32nd International Workshop on Statistical Modelling. Volume II. 2017: 243-246.
- Michelot T, Langrock R, Bestley S, Jonsen ID, Photopoulou T, Patterson TA. Estimation and simulation of foraging trips in land-based marine predators. Ecology. 2017;98(7):1932-1944.
- Langrock R, Borchers DL. Guest editors’ introduction to the special issue on “Ecological Statistics”. AStA Advances in Statistical Analysis. 2017;101(4):345-347.
- Hooten MB, King R, Langrock R. Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”. Journal of Agricultural, Biological and Environmental Statistics. 2017;22(3):224-231.
- Langrock R, Kneib T, Glennie R, Michelot T. Markov-switching generalized additive models. Statistics and Computing. 2017;27(1):259-270.
- Abanto-Valle CA, Langrock R, Chen M-H, Cardoso MV. Maximum likelihood estimation for stochastic volatility in mean models with heavy-tailed distributions. Applied Stochastic Models in Business and Industry. 2017;33(4):394–408.
- Leos-Barajas V, Gangloff EJ, Adam T, 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. 2017;22(3):232–248.
- Pohle JM, Langrock R. Pragmatic order selection in hidden Markov models. In: Proceedings of the 32nd IWSM Vol. 1. 2017.
- Pohle J, Langrock R, van Beest FM, Schmidt NM. Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement. Journal of Agricultural, Biological and Environmental Statistics. 2017;22(3):270–293.
- Patterson TA, Parton A, Langrock R, Blackwell PG, Thomas L, King R. Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges. AStA Advances in Statistical Analysis. 2017;101(4):399-438.
- Adam T, Leos-Barajas V, Langrock R, van Beest F. Using hierarchical hidden Markov models for joint inference at multiple temporal scales. In: Grzegorczyk M, Ceoldo G, eds. Proceedings of the 32nd International Workshop on Statistical Modelling, Vol. 2, Groningen, Netherlands 3-7 July, 2017. Groningen: Univ. of Groningen; 2017.
- Zucchini W, MacDonald IL, Langrock R. Hidden Markov Models for Time Series: An Introduction Using R. Monographs on statistics and applied probability. Vol 150 2nd ed. Boca Raton: CRC Press, Taylor & Francis; 2016.
- Michelot T, Langrock R, Patterson TA. moveHMM: An R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol Evol. 2016;7(11):1308-1315.
- Towner AV, Leos-Barajas V, Langrock R, et al. Sex-specific and individual preferences for hunting strategies in white sharks. Functional Ecology. 2016;30(8):1397-1407.
- Borchers DL, Langrock R. Double-observer line transect surveys with Markov-modulated Poisson process models for animal availability. Biometrics. 2015;71(4):1060-1069.
- Michelot T, Langrock R, Kneib T, King R. Maximum penalized likelihood estimation in semiparametric mark-recapture-recovery models. Biometrical Journal. 2015;58(1):222-239.
- Langrock R, Kneib T, Sohn A, DeRuiter SL. Nonparametric inference in hidden Markov models using P-splines. Biometrics. 2015;71(2):520-528.
- King R, Langrock R. Semi-Markov Arnason-Schwarz models. Biometrics. 2015;72(2):619-628.
- Langrock R, Michelot T, Sohn A, Kneib T. Semiparametric stochastic volatility modelling using penalized splines. Computational Statistics. 2015;30(2):517-537.
- Langrock R, Heidenreich N-B, Sperlich S. Kernel-based semiparametric multinomial logit modelling of political party preferences. Statistical Methods & Applications. 2014;23(3):435-449.
- Langrock R, Hopcraft JGC, Blackwell PG, et al. Modelling group dynamic animal movement. Methods in Ecology and Evolution. 2014;5(2):190-199.
- McKellar AE, Langrock R, Walters JR, Kesler DC. Using mixed hidden Markov models to examine behavioral states in a cooperatively breeding bird. Behavioral Ecology. 2014;26(1):148-157.
- Langrock R, Swihart BJ, Caffo BS, Punjabi NM, Crainiceanu CM. Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms. Statistics in Medicine. 2013;32(19):3342-3356.
- Langrock R, Borchers DL, Skaug HJ. Markov-modulated nonhomogeneous Poisson processes for modeling detections in surveys of marine mammal abundance. Journal of the American Statistical Association. 2013;108(503):840-851.
- Langrock R, King R. Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates. Ann. Appl. Stat. 2013;7(3):1709-1732.
- Langrock R, Marques TA, Baird RW, Thomas L. Modeling the Diving Behavior of Whales: A Latent-Variable Approach with Feedback and Semi-Markovian Components. Journal of Agricultural, Biological, and Environmental Statistics. 2013;19(1):82-100.
- Borchers DL, Zucchini W, Heide-Jørgensen MP, Cañadas A, Langrock R. Using hidden Markov models to deal with availability bias on line transect surveys. Biometrics. 2013;69(3):703-713.
- Langrock R, King R, Matthiopoulos J, Thomas L, Fortin D, Morales JM. Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology. 2012;93(11):2336-2342.
- Langrock R. Flexible Latent-State Modelling of Old Faithful's Eruption Inter-Arrival Times in 2009. Australian & New Zealand Journal of Statistics. 2012;54(3):261-279.
- Wichelhaus C, Langrock R. Nonparametric inference for stochastic feedforward networks based on cross-spectral analysis of point processes. Electronic Journal of Statistics. 2012;6:1670-1714.
- Schliehe-Diecks S, Kappeler PM, Langrock R. On the application of mixed hidden Markov models to multiple behavioural time series. Interface Focus. 2012;2(2):180-189.
- Langrock R, Zucchini W. Hidden Markov models with arbitrary state dwell-time distributions. Computational Statistics & Data Analysis. 2011;55(1):715-724.
- Langrock R. Some applications of nonlinear and non-Gaussian state–space modelling by means of hidden Markov models. Journal of Applied Statistics. 2011;38(12):2955-2970.
- Langrock R, MacDonald IL, Zucchini W. Some nonstandard stochastic volatility models and their estimation using structured hidden Markov models. Journal of Empirical Finance. 2011;19(1):147-161.
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