arXiv subjects

Gudmund H. Hermansen

Publications and source records attributed to Gudmund H. Hermansen.

3 recordsLinked to original sources

arXiv2026

Recent advances in statistical methodology applied to the Hjort liver index time series (1859-2012) and associated influential factors

Certain recent advances in statistical methodology have promising potential for fruitful use in general biology and the fisheries sciences. This paper reviews and discusses some of the relevant themes, including accurate modelling via focused model selection techniques, dynamic goodness-of-fit testing of processes evolving over time, finding break points for phenomena experiencing changes, prediction uncertainty, and optimal combination of information across diverse sources via confidence distributions. The methods are illustrated for the Hjort liver quality index time series. Its roots lie in the classic Hjort (`Fluctuations in the Great Fisheries of Northern Europe, Viewed in the Light of Biological Research', 1914), where liver quality of the Atlantic cod {\it (Gadus morhua)} for 1880--1912 is reported on and studied, along with related factors, making it one of the first teleost time series ever published. Diligent work by Kjesbu et al. (`Making use of Johan Hjort's `unknown' legacy: reconstruction of a 150-year coastal time-series on northeast Arctic cod (Gadus morhua) liver data reveals long-term trends in energy allocation patterns', 2014), involving both archival and calibration efforts, have extended the series both backwards and forwards in time, to 1859--2012, yielding one of the longest time series of marine science. Our study offers a detailed examination of this series and how it relates to and interacts with associated factors, including Kola winter temperatures, length distribution parameters, cod mortality, and a certain index related to availability of food.

stat.AP↗

arXiv2026

Conflict Forecasting via Conformal Prediction for Markov Processes

Whether or not a country is at war, or experiencing escalating or deescalating levels of conflict, has massive ramifications on a country's national and foreign policy. Given a country's history of conflict, or lack thereof, future predictions about the war-status of a country are valuable information. In this paper, we present the use of conformal prediction on temporally-dependent data to obtain prediction sets of possible future conflict state-sequences. More specifically, we compare the results of conformal prediction to a likelihood-based prediction strategy when the data are assumed to come from a discrete-state Markov process. A point-prediction may not supply sufficient information because the penalty for a wrong prediction is extreme, and so we consider a machine learning alternative that gives valid uncertainty quantification and is robust to model misspecification. In the data analysis, we present real forecasts of conflict dynamics across multiple countries. Lastly, we comment on the possible limitations of existing approaches for applying conformal prediction to Markovian data, where the exchangeability assumption is violated.

stat.ME↗

arXiv2024

Beyond time-homogeneity for continuous-time multistate Markov models

Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describe data that are observed irregularly over time, as is often the case in longitudinal medical data, for example. Assuming that a continuous-time Markov process is time-homogeneous, a closed-form likelihood function can be derived from the Kolmogorov forward equations -- a system of differential equations with a well-known matrix-exponential solution. Unfortunately, however, the forward equations do not admit an analytical solution for continuous-time, time-inhomogeneous Markov processes, and so researchers and practitioners often make the simplifying assumption that the process is piecewise time-homogeneous. In this paper, we provide intuitions and illustrations of the potential biases for parameter estimation that may ensue in the more realistic scenario that the piecewise-homogeneous assumption is violated, and we advocate for a solution for likelihood computation in a truly time-inhomogeneous fashion. Particular focus is afforded to the context of multistate Markov models that allow for state label misclassifications, which applies more broadly to hidden Markov models (HMMs), and Bayesian computations bypass the necessity for computationally demanding numerical gradient approximations for obtaining maximum likelihood estimates (MLEs). Supplemental materials are available online.

stat.CO↗