Per August Jarval Moen

• Moen, Per August Jarval (2027). Minimax rates in variance and covariance changepoint testing. Bernoulli. ISSN 1350-7265. To appear. doi: 10.48550/arXiv.2405.07757. Full text in Research Archive
• Moen, Per August Jarval; Nielsen, Sebastian Grau; Urheim, Espen Bjørge; Tveten, Martin & Glad, Ingrid Kristine (2026). gridcp: Fast Online Changepoint Detection in Python. arXiv. doi: 10.48550/ARXIV.2608.18695. Full text in Research Archive
• Moen, Per August Jarval (2026). A grid-based methodology for fast online changepoint detection. arXiv. doi: 10.48550/arXiv.2504.09573. Full text in Research Archive
• Christensen, Dennis & Moen, Per August Jarval (2025). perms: Likelihood-free estimation of marginal likelihoods for binary response data in Python and R. Journal of Computational Science. ISSN 1877-7503. 84. doi: 10.1016/j.jocs.2024.102467. Full text in Research Archive Show summary

  In Bayesian statistics, the marginal likelihood (ML) is the key ingredient needed for model comparison and model averaging. Unfortunately, estimating MLs accurately is notoriously difficult, especially for models where posterior simulation is not possible. Recently, Christensen (2023) introduced the concept of permutation counting, which can accurately estimate MLs of models for exchangeable binary responses. Such data arise in a multitude of statistical problems, including binary classification, bioassay and sensitivity testing. Permutation counting is entirely likelihood-free and works for any model from which a random sample can be generated, including nonparametric models. Here we present perms, a package implementing permutation counting. As a result of extensive optimisation efforts, perms is computationally efficient and able to handle large data problems. It is available as both an R package and a Python library. A broad gallery of examples illustrating its usage is provided, which includes both standard parametric binary classification and novel applications of nonparametric models, such as changepoint analysis. We also cover the details of the implementation of perms and illustrate its computational speed via a simple simulation study.

• Moen, Per August Jarval; Glad, Ingrid Kristine & Tveten, Martin (2024). Efficient sparsity adaptive changepoint estimation. Electronic Journal of Statistics. 18(2), p. 3975–4038. doi: 10.1214/24-EJS2294. Full text in Research Archive Show summary

  We propose a computationally efficient and sparsity adaptive procedure for estimating changes in unknown subsets of a high-dimensional data sequence. Assuming the data sequence is Gaussian, we prove that the new method successfully estimates the number and locations of changepoints with a given error rate and under minimal conditions for all sparsities of the changing subset. Our method has computational complexity linear up to logarithmic factors in both the length and number of time series, making it applicable to large data sets. Through extensive numerical studies we show that the new methodology is highly competitive in terms of both estimation accuracy and computational cost. The practical usefulness of the method is illustrated by analysing sensor data from a hydro power plant, and an efficient R implementation is available.

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• Moen, Per August Jarval (2026). A Grid-Based Methodology for Fast Online Changepoint Detection. Full text in Research Archive
• Moen, Per August Jarval (2026). A grid-based methodology for fast online changepoint detection. Full text in Research Archive
• Moen, Per August Jarval (2025). Advances in Change and Anomaly Detection: Challenges and Statistical Methods. Full text in Research Archive
• Moen, Per August Jarval (2025). A general methodology for fast online changepoint detection. Full text in Research Archive
• Christensen, Dennis & Moen, Per August Jarval (2023). Fast implementation of a general importance sampling algorithm for Bayesian nonparametric models with binary responses. Full text in Research Archive
• Moen, Per August Jarval (2023). High-dimensional change point estimation using thresholded CUSUM statistics. Full text in Research Archive
• Moen, Per August Jarval (2023). Efficient sparsity adaptive change point estimation. Full text in Research Archive
• Moen, Per August Jarval (2022). High-dimensional change-point estimation using thresholded CUSUM statistics. Full text in Research Archive
• Moen, Per August Jarval (2022). High-dimensional multiple change-point estimation using thresholded CUSUM statistics. Full text in Research Archive
• Løland, Anders; Tveten, Martin & Moen, Per August Jarval (2022). Episode 15: Krafthack 2022: Suksess med Rema 1000-strategi. [Internet]. Sannsynligvis VIKTIG (podkast). Full text in Research Archive

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