244 citations · 517 across the 23 of their papers we have counts for
26 papers
Nonparametric estimation of multivariate hidden Markov models using tensor-product B-splines
Rouven Michels, Roland Langrock
For multivariate time series driven by underlying states, hidden Markov models (HMMs) constitute a powerful framework which can be flexibly tailored to the situation at hand. Howev…
Dynamic Stochastic Inventory Management in E-Grocery Retailing
David Winkelmann, Matthias Ulrich, Michael Römer +2
E-grocery retailing enables ordering products online to be delivered at a future time slot chosen by the customer. This emerging field of business provides retailers with large and…
Bettors' reaction to match dynamics -- Evidence from in-game betting
Rouven Michels, Marius Ötting, Roland Langrock
It is still largely unclear to what extent bettors update their prior assumptions about the strength and form of competing teams considering the dynamics during the match. This is…
The reaction to news in live betting
Marius Ötting, Rouven Michels, Roland Langrock +1
Sports betting markets have grown very rapidly recently, with the total European gambling market worth 98.6 billion euro in 2019. Considering a high-resolution (1 Hz) data set prov…
Maximum approximate likelihood estimation of general continuous-time state-space models
Sina Mews, Roland Langrock, Marius Ötting +2
Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding…
A primer on coupled state-switching models for multiple interacting time series
Jennifer Pohle, Roland Langrock, Mihaela van der Schaar +2
State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying n…
Continuous-time multi-state capture-recapture models
Sina Mews, Roland Langrock, Ruth King +1
Multi-state capture-recapture data comprise individual-specific sighting histories together with information on individuals' states related, for example, to breeding status, infect…
Uncovering ecological state dynamics with hidden Markov models
Brett T. McClintock, Roland Langrock, Olivier Gimenez +4
Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities, or entire ecosystems through t…
A copula-based multivariate hidden Markov model for modelling momentum in football
Marius Ötting, Roland Langrock, Antonello Maruotti
We investigate the potential occurrence of change points - commonly referred to as "momentum shifts" - in the dynamics of football matches. For that purpose, we model minute-by-min…
Penalized estimation of flexible hidden Markov models for time series of counts
Timo Adam, Roland Langrock, Christian H. Weiß
Hidden Markov models are versatile tools for modeling sequential observations, where it is assumed that a hidden state process selects which of finitely many distributions generate…
Very Highly Skilled Individuals Do Not Choke Under Pressure: Evidence from Professional Darts
Christian Deutscher, Marius Ötting, Roland Langrock +3
Understanding and predicting how individuals perform in high-pressure situations is of importance in designing and managing workplaces, but also in other areas of society such as d…
The Hot Hand in Professional Darts
Marius Ötting, Roland Langrock, Christian Deutscher +1
We investigate the hot hand hypothesis in professional darts in a near-ideal setting with minimal to no interaction between players. Considering almost one year of tournament data,…
Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures
Vianey Leos-Barajas, Eric Gangloff, Timo Adam +4
Hidden Markov models (HMMs) are commonly used to model animal movement data and infer aspects of animal behavior. An HMM assumes that each data point from a time series of observat…
Selecting the Number of States in Hidden Markov Models - Pitfalls, Practical Challenges and Pragmatic Solutions
Jennifer Pohle, Roland Langrock, Floris van Beest +1
We discuss the notorious problem of order selection in hidden Markov models, i.e. of selecting an adequate number of states, highlighting typical pitfalls and practical challenges…
Estimation and simulation of foraging trips in land-based marine predators
Théo Michelot, Roland Langrock, Sophie Bestley +3
The behaviour of colony-based marine predators is the focus of much research globally. Large telemetry and tracking data sets have been collected for this group of animals, and are…
Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges
Toby A Patterson, Alison Parton, Roland Langrock +3
With the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry amo…
A multivariate mixed hidden Markov model to analyze blue whale diving behaviour during controlled sound exposures
Stacy L DeRuiter, Roland Langrock, Tomas Skirbutas +4
Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, then…
Analysis of animal accelerometer data using hidden Markov models
Vianey Leos-Barajas, Theoni Photopoulou, Roland Langrock +4
Use of accelerometers is now widespread within animal biotelemetry as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct obser…
Semi-Markov Arnason-Schwarz models
Ruth King, Roland Langrock
We consider multi-state capture-recapture-recovery data where observed individuals are recorded in a set of possible discrete states. Traditionally, the Arnason-Schwarz model has b…
Markov-switching generalized additive models
Roland Langrock, Thomas Kneib, Richard Glennie +1
We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regi…
Maximum penalized likelihood estimation in semiparametric capture-recapture models
Théo Michelot, Roland Langrock, Thomas Kneib +1
We discuss the semiparametric modeling of mark-recapture-recovery data where the temporal and/or individual variation of model parameters is explained via covariates. Typically, in…
Nonparametric inference in hidden Markov models using P-splines
Roland Langrock, Thomas Kneib, Alexander Sohn +1
Hidden Markov models (HMMs) are flexible time series models in which the distributions of the observations depend on unobserved serially correlated states. The state-dependent dist…
Modelling group dynamic animal movement
Roland Langrock, J. Grant C. Hopcraft, Paul G. Blackwell +7
Group dynamic movement is a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognised, part…
Semiparametric stochastic volatility modelling using penalized splines
Roland Langrock, Théo Michelot, Alexander Sohn +1
Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption…