TY  - JOUR
A1  - Pohle, Jennifer
A1  - Adam, Timo
A1  - Beumer, Larissa
T1  - Flexible estimation of the state dwell-time distribution in hidden semi-Markov models
JF  - Computational statistics & data analysis
N2  - Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the so-called dwell time, using some distribution defined on the natural numbers. While the (shifted) Poisson and negative binomial distribution provide natural choices for such distributions, in practice, parametric distributions can lack the flexibility to adequately model the dwell times. To overcome this problem, a penalised maximum likelihood approach is proposed that allows for a flexible and data-driven estimation of the dwell-time distributions without the need to make any distributional assumption. This approach is suitable for direct modelling purposes or as an exploratory tool to investigate the latent state dynamics. The feasibility and potential of the suggested approach is illustrated in a simulation study and by modelling muskox movements in northeast Greenland using GPS tracking data. The proposed method is implemented in the R-package PHSMM which is available on CRAN.
KW  - Penalized likelihood
KW  - Smoothing
KW  - Time series
KW  - Animal movement modeling
Y1  - 2022
U6  - https://doi.org/10.1016/j.csda.2022.107479
SN  - 0167-9473
SN  - 1872-7352
VL  - 172
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Gairing, Jan
A1  - Högele, Michael
A1  - Kosenkova, Tetiana
T1  - Transportation distances and noise sensitivity of multiplicative Levy SDE with applications
JF  - Stochastic processes and their application
N2  - This article assesses the distance between the laws of stochastic differential equations with multiplicative Levy noise on path space in terms of their characteristics. The notion of transportation distance on the set of Levy kernels introduced by Kosenkova and Kulik yields a natural and statistically tractable upper bound on the noise sensitivity. This extends recent results for the additive case in terms of coupling distances to the multiplicative case. The strength of this notion is shown in a statistical implementation for simulations and the example of a benchmark time series in paleoclimate.
KW  - Stochastic differential equations
KW  - Multiplicative Levy noise
KW  - Levy type processes
KW  - Heavy-tailed distributions
KW  - Model selection
KW  - Wasserstein distance
KW  - Time series
Y1  - 2017
U6  - https://doi.org/10.1016/j.spa.2017.09.003
SN  - 0304-4149
SN  - 1879-209X
VL  - 128
IS  - 7
SP  - 2153
EP  - 2178
PB  - Elsevier
CY  - Amsterdam
ER  -