If you made any changes in Pure these will be visible here soon.

Research Output 2008 2019

Filter
Journal article
2019

Leverage and influence diagnostics for Gibbs spatial point processes

Baddeley, A., Rubak, E. & Turner, R., Mar 2019, In : Spatial Statistics. 29, p. 15-48 34 p.

Research output: Contribution to journalJournal articleResearchpeer-review

Influence Diagnostics
Spatial Point Process
Composite Likelihood
Leverage
Diagnostics
3 Citations (Scopus)

Resample-smoothing of Voronoi intensity estimators

Moradi, M. M., Cronie, O., Rubak, E., Lachieze-Rey, R., Mateu, J. & Baddeley, A., 19 Jan 2019, In : Statistics and Computing. 16 p.

Research output: Contribution to journalJournal articleResearchpeer-review

Open Access
Voronoi
Smoothing
Estimator
Linear networks
Highway accidents
2018
12 Citations (Scopus)

Determinantal point process models on the sphere

Møller, J., Nielsen, M., Porcu, E. & Rubak, E. H., 2018, In : Bernoulli. 24, 2, p. 1171-1201 31 p.

Research output: Contribution to journalJournal articleResearchpeer-review

Point Process
Process Model
Covariance Function
kernel
Moment
2016
3 Citations (Scopus)

Adjusted composite likelihood ration test for spatial Gibbs point processes

Baddeley, A., Turner, R. & Rubak, E. H., 2016, In : Journal of Statistical Computation and Simulation. 86, 5, p. 922-941 20 p.

Research output: Contribution to journalJournal articleResearchpeer-review

5 Citations (Scopus)

Functional summary statistics for point processes on the sphere with an application to determinantal point processes

Møller, J. & Rubak, E. H., Nov 2016, In : Spatial Statistics. 18, Part A, p. 4-23 20 p.

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)

Mechanistic Spatio-Temporal Point Process Models for Marked Point Processes, with a View to Forest Stand Data

Møller, J., Ghorbani, M. & Rubak, E. H., Sep 2016, In : Biometrics. 72, 3, p. 687-696 10 p.

Research output: Contribution to journalJournal articleResearchpeer-review

2015
63 Citations (Scopus)

Determinantal point process models and statistical inference

Lavancier, F., Møller, J. & Rubak, E. H., 2015, In : Journal of the Royal Statistical Society, Series B (Statistical Methodology). 77, 4, p. 853-877 25 p.

Research output: Contribution to journalJournal articleResearchpeer-review

2014
23 Citations (Scopus)

Logistic regression for spatial Gibbs point processes

Baddeley, A., Coeurjolly, J-F., Rubak, E. H. & Waagepetersen, R., 2014, In : Biometrika. 101, 2, p. 377-392 16 p.

Research output: Contribution to journalJournal articleResearchpeer-review

2013
10 Citations (Scopus)

Fast covariance estimation for innovations computed from a spatial Gibbs point process

Coeurjolly, J. & Rubak, E. H., 2013, In : Scandinavian Journal of Statistics. 40, 4, p. 669-684 16 p.

Research output: Contribution to journalJournal articleResearchpeer-review

2011
15 Citations (Scopus)

Score, pseudo-score and residual diagnostics for spatial point process models.

Baddeley, A., Rubak, E. H. & Møller, J., 2011, In : Statistical Science. 26, 4, p. 613-646 34 p.

Research output: Contribution to journalJournal articleResearchpeer-review

2010
2 Citations (Scopus)

A Model for Positively Correlated Count Variables

Møller, J. & Rubak, E. H., 2010, In : International Statistical Review. 78, 1, p. 65-80 16 p.

Research output: Contribution to journalJournal articleResearchpeer-review

Random Field
Count
Random variable
Non-negative
Model