16 citations · 31 across the 6 of their papers we have counts for
6 papers
How to build your latent Markov model -- the role of time and space
Sina Mews, Jan-Ole Koslik, Roland Langrock
Statistical models that involve latent Markovian state processes have become immensely popular tools for analysing time series and other sequential data. However, the plethora of m…
Inference on the state process of periodically inhomogeneous hidden Markov models for animal behavior
Jan-Ole Koslik, Carlina C. Feldmann, Sina Mews +2
Over the last decade, hidden Markov models (HMMs) have become increasingly popular in statistical ecology, where they constitute natural tools for studying animal behavior based on…
Markov-modulated marked Poisson processes for modelling disease dynamics based on medical claims data
Sina Mews, Bastian Surmann, Lena Hasemann +1
We explore Markov-modulated marked Poisson processes (MMMPPs) as a natural framework for modelling patients' disease dynamics over time based on medical claims data. In claims data…
Continuous-time state-space modelling of the hot hand in basketball
Sina Mews, Marius Ötting
We investigate the hot hand phenomenon using data on 110,513 free throws taken in the National Basketball Association (NBA). As free throws occur at unevenly spaced time points wit…
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…
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…