Hans J. Skaug - researchr alias

researchr

You are not signed in
Sign in
Sign up

Viewing Publication 1 - 9 from 9

2023

A TMB Approach to Study Spatial Variation in Weather-Generated Claims in InsuranceIngrid Sandvig Thorsen, Bård Støve, Hans J. Skaug. orf, 4(4):79, December 2023. [doi]

2022

Epistemic uncertainty quantification in deep learning classification by the Delta methodGeir Kjetil Nilsen, Antonella Zanna Munthe-Kaas, Hans J. Skaug, Morten Brun. NN, 145:164-176, 2022. [doi]

2018

On the application of improved symplectic integrators in Hamiltonian Monte CarloJanne Mannseth, Tore Selland Kleppe, Hans J. Skaug. cssc, 47(2):500-509, 2018. [doi]

2017

glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed ModelingMollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen 0002, Hans J. Skaug, Martin Mächler, Benjamin M. Bolker. rjour, 9(2):378, 2017. [doi]

2016

Bandwidth selection in pre-smoothed particle filtersTore Selland Kleppe, Hans J. Skaug. sac, 26(5):1009-1024, 2016. [doi]

2014

A flexible and automated likelihood based framework for inference in stochastic volatility modelsHans J. Skaug, Jun Yu. csda, 76:642-654, 2014. [doi]

Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes MethodSimone Vincenzi, Marc Mangel, Alain J. Crivelli, Stephan Munch, Hans J. Skaug. ploscb, 10(9), 2014. [doi]

2012

AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear modelsDavid A. Fournier, Hans J. Skaug, Johnoel Ancheta, James Ianelli, Arni Magnusson, Mark N. Maunder, Anders Nielsen, John Sibert. oms, 27(2):233-249, 2012. [doi]

2006

Automatic approximation of the marginal likelihood in non-Gaussian hierarchical modelsHans J. Skaug, David A. Fournier. csda, 51(2):699-709, 2006. [doi]

Sign in or sign up to see more results.

runs on WebDSL