Random effects in (open population) spatial capture-recapture models
Click here to find out more about (open population) spatial capture-recapture models.
Hierarchical hidden Markov models for multi-scale time series
Basic hidden Markov models (HMMs) are restricted to modeling single-scale data. In practice, however, variables are often observed at different temporal resolutions: an economy’s gross domestic product, e.g., is typically observed on a yearly, quarterly, or monthly basis, whereas stock prices are available daily or at even finer resolutions. By regarding the observations as stemming from multiple, connected state processes, hierarchical HMMs allow to jointly model multi-scale data. Click here to find out more about hierarchical HMMs.
Markov-switching generalised additive models for location, scale, and shape
Markov-switching generalized additive models for location, scale, and shape (Markov-switching GAMLSS) constitute a novel class of flexible latent-state time series regression models. In contrast to basic Markov-switching regression models, they can be used to model different state-dependent parameters of the response distribution (not only the mean, but also e.g. the variance) as potentially smooth functions of explanatory variables. Click here to find out more about Markov-switching GAMLSS.