Per August Jarval Moen

Doctoral Research Fellow - Statistics and Data Science

$Image of Per August Jarval Moen$

Norwegian version of this page

Email pamoen@math.uio.no

Room 802

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Visiting address Niels Henrik Abels hus

Postal address Postboks 1053 Blindern 0316 Oslo

Other affiliations Faculty of Mathematics and Natural Sciences (Student)

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Foto: Kim Ramberghaug

Academic interests

My PhD project is about changepoint and anomaly detection in high-dimensional data streams. My supervisors are prof. Ingrid Glad (UiO) and Martin Tveten (NR).

Teaching

From January 2023: co-supervising Han Yu's master's project "Modeling sensor data from a hydropower plant for the purpose of anomaly detection" together with Ingrid Glad
Fall 2022: Teaching assistant in STK3100/STK4100 - Generalized Linear Models
Spring 2022: Teaching assistant in STK4900 – Statistical Methods and Applications.
Fall 2021: Teaching assistant in STK3100/STK4100 - Generalized Linear Models

Other

A guide to calling C functions in R (C source code can be found here).

Background

I have two bachelor's degrees from the University of Oslo; Mathematics with Informatics and Mathematics and Economics. I did my master's in Mathematical Statistics at King's College, University of Cambridge.

Tags: statistics and data science

Moen, Per August Jarval; Glad, Ingrid Kristine & Tveten, Martin (2023). Efficient sparsity adaptive changepoint estimation. arXiv. doi: 10.48550/arXiv.2306.04702.

View all works in Cristin

Moen, Per August Jarval (2023). Efficient sparsity adaptive change point estimation.
Moen, Per August Jarval (2023). High-dimensional change point estimation using thresholded CUSUM statistics.
Christensen, Dennis & Moen, Per August Jarval (2023). Fast implementation of a general importance sampling algorithm for Bayesian nonparametric models with binary responses.
Christensen, Dennis & Moen, Per August Jarval (2023). perms: Likelihood-free estimation of marginal likelihoods for binary response data in Python and R. arXiv.org. ISSN 2331-8422. doi: 10.48550/arXiv.2309.01536. 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 (2022). High-dimensional change-point estimation using thresholded CUSUM statistics.
Moen, Per August Jarval (2022). High-dimensional multiple change-point estimation using thresholded CUSUM statistics.
Løland, Anders; Tveten, Martin & Moen, Per August Jarval (2022). Episode 15: Krafthack 2022: Suksess med Rema 1000-strategi. [Internet]. Sannsynligvis VIKTIG (podkast).