Steinar Engen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Steinar Engen has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Erik Blystad Solbu
Dept of Mathematical Sciences, Centre for Biodiversity Dynamics Norwegian Univ. of Science and Technology NO‐7491 Trondheim Norway
1 shared publication - Erik Blystad Solbu
Norwegian Institute of Bioeconomy Research
1 shared publication - John A. Kålås
Norwegian Institute for Nature Research, P.O. Box 5685 Torgard, NO-7485 Trondheim, Norway
1 shared publication - Magnar LillegÅRd
Department of Mathematics and Statistics, Norwegian University of Science and Technology N-7055 Dragvoll, Norway
1 shared publication Ola Håvard Diserud
Norwegian Institute for Nature Research, N-7485 Trondheim, Norway
1 shared publication- Ola Håvard Diserud
Norwegian Institute for Nature Research (NINA) Trondheim Norway
1 shared publication
Latest Publications
- 27 September 2018
- Published by Elsevier in Ecological Modelling
- Vol. 388, 13-23
Heterogeneity among species and community dynamics—Norwegian bird communities as a case study
- 27 September 2018
- Published by Elsevier in Ecological Modelling
- Vol. 388, 13-23
Heterogeneity among species and community dynamics—Norwegian bird communities as a case study
- 1 October 2016
- Published by Elsevier in Theoretical Population Biology
- Vol. 111, 65-74
Characteristics of temporal changes in communities where dynamics differ between species
- 1 October 2016
- Published by Elsevier in Theoretical Population Biology
- Vol. 111, 65-74
Characteristics of temporal changes in communities where dynamics differ between species
- 1 March 1997
- Published by Oxford University Press (OUP) in Biometrika
- Vol. 84 (1) , 235-240
Miscellanea. Stochastic simulations conditioned on sufficient statistics
A general method for doing Monte Carlo simulations conditioned on sufficient statistics is presented. The basic idea is to adjust the parameter values in the corresponding unconditional simulation so that the actual value of the sufficient statistic is obtained. In the case of multiple solutions to this problem, the method has to be modiiied, even if the corresponding value of the simulated variable is unique for each simulation. The methods are illustrated for some simple models in which the conditional distributions are well known. As a more complicated example, an exact ‘analysis of variance’ test in the gamma model is performed. Other examples are a modifimtion of the Kolmogorov goodness-of-fit test into an exact test, and minimum variance unbiased estimation of cumulative distributions.
- 1 March 1997
- Published by Oxford University Press (OUP) in Biometrika
- Vol. 84 (1) , 235-240
Miscellanea. Stochastic simulations conditioned on sufficient statistics
A general method for doing Monte Carlo simulations conditioned on sufficient statistics is presented. The basic idea is to adjust the parameter values in the corresponding unconditional simulation so that the actual value of the sufficient statistic is obtained. In the case of multiple solutions to this problem, the method has to be modiiied, even if the corresponding value of the simulated variable is unique for each simulation. The methods are illustrated for some simple models in which the conditional distributions are well known. As a more complicated example, an exact ‘analysis of variance’ test in the gamma model is performed. Other examples are a modifimtion of the Kolmogorov goodness-of-fit test into an exact test, and minimum variance unbiased estimation of cumulative distributions.