Geir Drage Berentsen Photo: Hallvard Lyssand

Professor Geir Drage Berentsen

E-mail: Geir.Berentsen@nhh.no
Telephone: +47 55 95 94 65
Department: Business and Management Science
Office: C510
Expertise: Management Science Time Series & Longitudinal Data Measures of Dependence Spatial Models Computational Statistics

Biography and CV Selected publications Research Dissemination

Geir Drage Berentsen received his PhD in statistics from the University of Bergen in 2013 on a dissertation concerning the modelling and measurement of nonlinear dependence between random variables. In 2013 – 2016 he worked as a consultant in Safetec Nordic AS, a company that supplies a wide range of risk management services. He then returned to the Department of Mathematics working as a postdoctor.

He is now an Associate Professor at NHH, and his research interests include count time series, longitudinal data and spatial models with applications in finance, medicine and econometrics.

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Selected publications

Author(s)	Title	Publisher
Hølleland, Sondre Nedreås; Berentsen, Geir Drage; Otneim, Håkon; Solbrekke, Ida Marie	Optimal allocation of 30 GW offshore wind power in the Norwegian economic zone	Wind Energy Science Volume 10 (1); page 293 - 313; 2025
Bacri, Timothee Raphael Ferdinand; Berentsen, Geir Drage; Bulla, Jan; Støve, Bård	Computational issues in parameter estimation for hidden Markov models with template model builder	Journal of Statistical Computation and Simulation Volume 93 (18); page 3421 - 3457; 2023
Azzolini, Francesca; Berentsen, Geir Drage; Skaug, Hans Julius; Hjelmborg, Jacob V.B.; Kaprio, Jaakko A.	The heritability of BMI varies across the range of BMI—a heritability curve analysis in a twin cohort	International Journal of Obesity Volume 46; page 1786 - 1791; 2022
Bacri, Timothee Raphael Ferdinand; Berentsen, Geir Drage; Bulla, Jan; Hølleland, Sondre Nedreås	A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using Template Model Builder	Biometrical Journal; 2022
Berentsen, Geir Drage; Bulla, Jan; Maruotti, Antonello; Støve, Bård	Modelling clusters of corporate defaults: Regime-switching models significantly reduce the contagion source	Journal of the Royal Statistical Society, Series C (Applied Statistics) Volume 71 (3); page 1 - 698; 2022
Otneim, Håkon; Berentsen, Geir Drage; Tjøstheim, Dag Bjarne	Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis	Entropy Volume 24 (3) (17 pages); 2022
Sleire, Anders Daasvand; Støve, Bård; Otneim, Håkon; Berentsen, Geir Drage; Tjøstheim, Dag Bjarne; Haugen, Sverre Hauso	Portfolio allocation under asymmetric dependence in asset returns using local Gaussian correlations	Finance Research Letters; page 1 - 9; 2021
Mannseth, Janne; Berentsen, Geir Drage; Skaug, Hans Julius; Lie, Rolv T.; Moster, Dag	Variation in use of Caesarean section in Norway: An application of spatio-temporal Gaussian random fields	Scandinavian Journal of Public Health (8 pages); 2021
Berentsen, Geir Drage; Azzolini, Francesca; Skaug, Hans J.; Lie, Rolv T.; Gjessing, Håkon K.	Heritability curves: A local measure of heritability in family models	Statistics in Medicine Volume 40 (6); page 1357 - 1382; 2020
Berentsen, Geir Drage; Cao, Ricardo; Francisco-Fernandez, Mario; Tjøstheim, Dag Bjarne	Some properties of local Gaussian correlation and other nonlinear dependence measures	Journal of Time Series Analysis Volume 38 (2); page 352 - 380; 2016
Berentsen, Geir Drage; Støve, Bård; Tjøstheim, Dag Bjarne; Nordbø, Tommy Neverdahl	Recognizing and visualizing copulas: An approach using local Gaussian approximation	Insurance, Mathematics & Economics Volume 57 (1); page 90 - 103; 2014
Berentsen, Geir Drage; Tjøstheim, Dag Bjarne	Recognizing and visualizing departures from independence in bivariate data using local Gaussian correlation	Statistics and computing Volume 24 (5); page 785 - 801; 2014
Berentsen, Geir Drage; Kleppe, Tore Selland; Tjøstheim, Dag Bjarne	Introducing localgauss, an R package for estimating and visualizing local Gaussian correlation	Journal of Statistical Software Volume 56 (12); page 1 - 18; 2014