activity
20242026
collaborators

9 papers

stat.ME2026

Non-Homogeneous Markov-Switching Generalized Additive Models for Location, Scale, and Shape

Katharina Ammann, Timo Adam, Jan-Ole Koslik

We propose an extension of Markov-switching generalized additive models for location, scale, and shape (MS-GAMLSS) that allows covariates to influence not only the parameters of th…

stat.ME2025

Flexible unimodal density estimation in hidden Markov models

Jan-Ole Koslik, Fanny Dupont, Marie Auger-Méthé +3

1. Hidden Markov models (HMMs) are powerful tools for modelling time-series data with underlying state structure. However, selecting appropriate parametric forms for the state-depe…

stat.ME2025

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…

stat.ME2025

Tensor-product interactions in Markov-switching models

Jan-Ole Koslik

Markov-switching models are a powerful tool for modelling time series data that are driven by underlying latent states. As such, they are widely used in behavioural ecology, where…

stat.ME2025

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…

stat.ME2024

Efficient smoothness selection for nonparametric Markov-switching models via quasi restricted maximum likelihood

Jan-Ole Koslik

Markov-switching models are powerful tools that allow capturing complex patterns from time series data driven by latent states. Recent work has highlighted the benefits of estimati…