182. Talk at an invited session at 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022), London, UK, Dec, 2022.
183. Invited (opening-)talk at the workshop INLA: past, present, and future (18-19th Jan 2023), Univof Glasgow, Scotland.
184. Three day course on Spatial and Spatio-temporal models with R-INLA, University of Navarra,
Pamplona, Spain, January 2023 (with E. Krainksi)
185. Presentation at AMCS-STAT School, KAUST, March 2023.
186. Six lectures at the Workshop on Bayesian Modeling for Complex Correlated Data, Valencia, Spain,
May 2023.
187. Invited Department seminar, at the University of Valencia, Spain, May 2023.
188. Invited (double) talk at the Workshop for Bayes-duality, RIKEN, Tokyo, Japan, June 2023.
189. Invited seminar, University of Glasgow, October, 2023
190. Invited seminar, University of Edinburgh, October, 2023
191. Presentation of invited discussion article in SORT, Catalan Statistical Society, Barcelona, April,
2024.
192. Invited (opening) talk at the Workshop on Hierarchical Bayesian Spatio-Temporal modelling, Univ
of Valencia, April, 2024.
193. Invited Zoom-seminar at Department of Statistics, Universidade Federal de Minas Gerais,
Brazil, May 2024.
194. Invited talk at 2nd Bayes-Duality Workshop, RIKEN, Tokyo, Japan, June 2024
195. Participated in the Royal Statistical Society Conference, Brighton, UK, Sep, 2024
196. Invited keynote talk at the 2nd International Conference on Sciences, Technology, and Education(ICSTE) 2024, Universitas Negeri Makassar, Indonesia, Sep 2024
197. Participated at the GeoMED 2024 conference, Hasselt, Belgium, Sep 2024

Books[55]

H. Rue and L. Held. Gaussian Markov Random Fields: Theory and Applications. Vol. 104. Monographs on Statistics and Applied Probability. London: Chapman & Hall, 2005.

[155]

E. T. Krainski, V. Gómez-Rubio, H. Bakka, A. Lenzi, D. Castro-Camilio, D. Simpson, F. Lindgren, and H. Rue. Advanced Spatial Modeling with Stochastic Partial Differential Equations using
R and INLA. Github version www.r-inla.org/spde-book. CRC press (Translated to Chinese2024), Dec. 2018.

Read papers and discussion papers[74]

H. Rue, S. Martino, and N. Chopin. “Approximate Bayesian Inference for Latent Gaussian
Models Using Integrated Nested Laplace Approximations (with discussion)”. In: Journal of
the Royal Statistical Society, Series B 71.2 (2009), pp. 319–392.

[89]

F. Lindgren, H. Rue, and J. Lindström. “An explicit link between Gaussian fields and Gaussian Markov random fields: The SPDE approach (with discussion)”. In: Journal of the Royal
Statistical Society, Series B 73.4 (2011), pp. 423–498.

[93]

J. Wyse, N. Friel, and H. Rue. “Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments (with discussion)”. In: Bayesian
Analysis 6.4 (2011), pp. 501–546.

[146]

D. P. Simpson, H. Rue, A. Riebler, T. G. Martins, and S. H. Sørbye. “Penalising model component complexity: A principled, practical approach to constructing priors (with discussion)”.
In: Statistical Science 32.1 (2017), pp. 1–28.

[232]

F. Lindgren, H. Bakka, D. Bolin, E. Krainski, and H. Rue. “A diffusion-based spatio-temporalextension of Gaussian Matérn fields (with discussion)”. In: SORT 48.1 (2024), pp. 3–66. DOI:
10.57645/20.8080.02.13.

Publications 2024
[223]

A. Adin, E. Krainski, A. Lenzi, Z. Liu, J. Martínez-Minaya, and H. Rue. “Automatic crossvalidation in structured models: Is it time to leave out leave-one-out?” In: Spatial Statistics (toappear) 62.Aug (2024). DOI: https://doi.org/10.1016/j.spasta.2024.100843.

[224]

D. Alvares, D. Rustand, E. T. Krainski, J. van Niekerk, and H. Rue. “Bayesian survival analysis with INLA”. In: Statistics in Medicine 43.20 (2024), pp. 3975–4010. DOI: https://doi.
org/10.1002/sim.10160.

[225]

A. O. Batouche, D. Rustand, E. Czeizler, H. Rue, T. Mirtti, and A. Rannikko. “A joint frailty
model to assess the relationship between time to curative treatment and biochemical recurrence in Prostate Cancer patients”. In: Computers in Biology and Medicine (submitted) xx.xx(2024), pp. xx–xx.

[226]

J. Belmont, S. Martino, J. Illian, and H. Rue. “Spatio-temporal Occupancy Models with INLA”.
In: Methods in Ecology and Evolution xx.xx (2024), pp. xx–xx.

[227]

R. Cabral, D. Bolin, and H. Rue. “Robustness, model checking and latent Gaussian models(to appear)”. In: Journal of the Royal Statistical Society, Series B xx.xx (2024), pp. xx–xx.

[228]

M. de Bie, J. Van Niekerk, A. Bekker, and H Rue. “Spatio-temporal insights for wind energy harvesting in South Africa”. In: International Journal on Geomathematics (submitted) xx.xx(2024), pp. xx–xx.

[229]

E. A. Fattah, E. Krainski, J. van Niekerk, and H. Rue. “Non-stationary Bayesian Spatial Modelfor Disease Mapping based on Sub-regions”. In: Statistical Methods in Medical Research (accepted) xx.xx (xx 2024), pp. xx–xx. URL: https://arxiv.org/abs/2306.17236.

[230]

L. Gaedke-Merzhäuser, E. Krainski, R. Janalik, H. Rue, and O. Schenk. “Integrated Nested
Laplace Approximations for Large-Scale Spatial-Temporal Bayesian Modeling”. In: SIAM
Journal on Scientific Computing 46.4 (2024), B448–B473. DOI: 10.1137/23M1561531. eprint:
https : / / doi . org / 10 . 1137 / 23M1561531. URL: https : / / doi . org / 10 . 1137 /23M1561531.

[231]

F. Lindgren, F. Bachl, J. Illian, M. H. Suen, H. Rue, and A. E. Seaton. “inlabru: software for
fitting latent Gaussian models with non-linear predictors”. In: Submitted xx.xx (2024), pp. xx–
xx. arXiv: 2407.00791 [stat.ME]. URL: https://arxiv.org/abs/2407.00791.

[232]

F. Lindgren, H. Bakka, D. Bolin, E. Krainski, and H. Rue. “A diffusion-based spatio-temporalextension of Gaussian Matérn fields (with discussion)”. In: SORT 48.1 (2024), pp. 3–66. DOI:
10.57645/20.8080.02.13.

[233]

J. Martińez-Minaya and H. Rue. “A flexible Bayesian tool for CoDa mixed models: logisticnormal distribution with Dirichlet covariance”. In: Statistics and Computing (to appear) 35.116(2024), pp. xx–xx. DOI: https://doi.org/10.1007/s11222-024-10427-3.

[234]

D. Rustand, J. van Niekerk, E. T. Krainski, and H. Rue. “Joint Modeling of Multivariate Longitudinal and Survival Outcomes with the R package INLAjoint”. In: (submitted) xx.xx (2024),
pp. xx–xx. arXiv: 2402 . 08335 [stat.ME]. URL: https : / / arxiv . org / abs / 2402 .
08335.

[235]

E. Ŝtrumbelj, A. Bouchard-Côté, J. Corander, A. Gelman, H. Rue, L. Murray, H. Pesonen, M.
Plummer, and A. Vehtari. “Past, Present, and Future of Software for Bayesian Inference”. In:
Statistical Science 39.1 (2024), pp. 46–61. ISSN: 0883-4237. DOI: 10.1214/23-STS907.

[236]

J. van Niekerk and H. Rue. “Low-rank Variational Bayes correction to the Laplace Method”.
In: Journal of Machine Learning Research 25.62 (2024), pp. 1–25. eprint: 2111 . 12945. URL:
https://jmlr.org/papers/v25/21-1405.html.

Publications 2023
[206]

H. Alahmadi, J. van Niekerk, T. Padellini, and H. Rue. “Joint Quantile Disease Mapping with
Application to Malaria and G6PD Deficiency”. In: Royal Society Open Science xx.xx (2023). (toappear), pp. xx–xx.

[207]

R. Cabral, D. Bolin, and H. Rue. “Controlling the Flexibility of Non-Gaussian Processes
Through Shrinkage Priors”. In: Bayesian Analysis 18.4 (2023), pp. 1223–1246. DOI: 10.1214/
22-BA1342. URL: https://doi.org/10.1214/22-BA1342.

[208]

R. Cabral, D. Bolin, and H. Rue. “Fitting latent non-Gaussian models using variational Bayesand Laplace approximations”. In: Journal of the American Statistical Association xx.xx (2023),
pp. xx–xx. DOI: 10.1080/01621459.2023.2296704.

[209]

F. Chao, B. Masquelier, D. You, L. Hug, Y. Liu, D. Sharrow, H. Rue, H. Ombao, and L. Alkema.
“Sex differences in survival chances among children, adolescents and youth aged 0-24: asystematic assessment of national, regional and global trends from 1990 to 2021”. In: The
LANCET Global Health 11.10 (2023), e1519–e1530. ISSN: 2214-109X. DOI: https : / / doi .
org / 10 . 1016 / S2214 - 109X(23 ) 00376 - 5. URL: https : / / www . sciencedirect .
com/science/article/pii/S2214109X23003765.

[210]

S. Chaudhuria, M. A. Barcelóa, P. Juana, D. Vargaa, D. Bolin, H. Rue, and M. Saez. “Enhanced
spatial modeling on linear networks using Gaussian Whittle-Matérn fields”. In: (submitted)
xx.xx (2023), pp. xx–xx. URL: https://arxiv.org/abs/xxxx.xxxxxx.

[211]

C. Chiuchiolo, J. van Niekerk, and H. Rue. “Joint Posterior Inference for Latent Gaussian
Models with R-INLA”. In: Journal of Statistical Computation and Simulation 93.5 (2023), pp. 723–
752. DOI: https://doi.org/10.1080/00949655.2022.2117813.

[212]

A. Freni-Sterrantino, D. Rustand, J. van Niekerk, E. Krainski, and H. Rue. “A Graphical
Framework for Interpretable Correlation Matrix Models”. In: (submitted) xx.xx (2023), pp. xx–
xx. URL: https://arxiv.org/abs/xxxx.xxxxxx.

[213]

M. E. Khan and H. Rue. “The Bayesian Learning Rule”. In: Journal of Machine Learning Research 24 (2023). https://jmlr.org/papers/volume24/22-0291/22-0291.pdf, pp. 1–46.

[214]

A. Lenzi and H. Rue. “Towards black-box parameter estimation”. In: (submitted) xx.xx (xx2023), pp. xx–xx. URL: http://arxiv.org/abs/2303.15041.

[215]

F. Palmí-Perales, V. Gómez-Rubio, R. S. Bivand, M. Cameletti, and H. Rue. “Bayesian Inference for Multivariate Spatial Models with INLA”. In: The R Journal 15.3 (2023), pp. 172–190.
URL : https://rjournal.github.io/articles/RJ-2023-068.

[216]

I. Papastathopoulos, L. de Monte, R. Campbell, and H. Rue. “Statistical inference for radially stable generalized Pareto distributions and return level-sets in geometric extremes”. In:(subitted) xx.xx (2023), pp. xx–xx. URL: https://arxiv.org/abs/2310.06130.

[217]

Z. C. Quiroz, M. O. Prates, D. K. Dey, and H. Rue. “Fast Bayesian inference of Block Nearest
Neighbor Gaussian process for large data”. In: Statistics and Computing xx.xx (xx 2023). (toappear), pp. xx–xx.

[218]

D. Rustand, J. Van Niekerk, E. T. Krainski, H. Rue, and C. Proust-Lima. “Fast and flexible
inference approach for joint models of multivariate longitudinal and survival data using
Integrated Nested Laplace Approximations”. In: Biostatistics xx.xx (2023), pp. xx–xx. DOI:
https://doi.org/10.1093/biostatistics/kxad019.

[219]

D. Rustand, J. Van Niekerk, H. Rue, C. Tournigand, V. Rondeau, and L. Briollais. “Bayesian
Estimation of Two-Part Joint Models for a Longitudinal Semicontinuous Biomarker and a
Terminal Event with R-INLA: Interests for Cancer Clinical Trial Evaluation”. In: Biometrical Journal xx.xx (xx 2023), pp. xx–xx. DOI: https : / / doi . org / 10 . 1002 / bimj .
202100322. eprint: https : / / onlinelibrary . wiley . com / doi / pdf / 10 . 1002 /bimj.202100322. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/
bimj.202100322.

[220]

J. van Niekerk, E. Krainksi, D. Rustand, and H. Rue. “A new avenue for Bayesian inferencewith INLA”. In: Computational Statistics & Data Analysis 181 (May 2023), p. 107692.

[221]

Z. Zhang, E. Krainski, P. Zhong, H. Rue, and R. Huser. “Joint Modeling and Prediction of
Massive Spatio-Temporal Wildfire Count and Burnt Area Data with the INLA-SPDE Approach”. In: Extremes (to appear) xx.xx (2023). https://arxiv.org/abs/2202.06502, pp. xx–xx.

[222]

A. Zhumekenov, E. T. Krainski, and H. Rue. “Parallel Selected Inversion for Space-Time
Gaussian Markov Random Fields”. In: (submitted) xx.xx (2023), pp. xx–xx. URL: https://
arxiv.org/abs/2309.05435.

Publications 2022
[192]

M. O. Berild, S. Martino, V. Gomez-Rubio, and H. Rue. “Importance Sampling with the Integrated Nested Laplace Approximation”. In: Journal of Computational and Graphical Statistics
xx.xx (2022). (accepted), pp. xx–xx. URL: https://arxiv.org/abs/2103.02721.

[193]

D. Castro-Camilio, R. Huser, and H. Rue. “Practical strategies for GEV-based regression
models for extremes”. In: Environmetrics (2022). (accepted), pp. xx–xx. DOI: doi.org/10.
1002/env.27.

[194]

C. Chiuchiolo, J. van Niekerk, and H. Rue. “An Extended Simplified Laplace strategy for
Approximate Bayesian inference of Latent Gaussian Models using R-INLA”. In: (submitted)
xx.xx (2022). https://arxiv.org/abs/2203.14304, pp. xx–xx. arXiv: 2203.14304 [stat.ME].

[195]

E. A. Fattah and H. Rue. “Approximate Bayesian Inference for the Interaction Types 1, 2, 3
and 4 with Application in Disease Mapping”. In: (submitted) xx.xx (2022), pp. xx–xx. DOI:
10.48550/ARXIV.2206.09287. URL: https://arxiv.org/abs/2206.09287.

[196]

E. A. Fattah, J. Van Niekerk, and H. Rue. “Smart Gradient – An Adaptive Technique for
Improving Gradient Estimation”. In: Foundations of Data Science 4.1 (Mar. 2022), pp. 123–136.
DOI : 10.3934/fods.2021037.

[197]

M. Franco-Villoria, M. Ventrucci, and H. Rue. “Variance partitioning in spatio-temporal disease mapping models”. In: Statistical Methods in Medical Research 31.8 (2022), pp. 1566–1578.

[198]

L. Gaedke-Merzhäuser, J. van Niekerk, O. Schenk, and H. Rue. “Parallelized integrated
nested Laplace approximations for fast Bayesian inference”. In: Statistics and Computing 33.1
(2022). DOI: 10.1007/s11222-022-10192-1.

[199]

H. Hammer, A. Yazidi, M. A. Riegler, and H. Rue. “Efficient Quantile Tracking Using an
Oracle”. In: Applied Intelligence xx.xx (2022). (to appear), pp. xx–xx. URL: arXiv : 2004 .
12588.

[200]

S. Hunanyan, H. Rue, M. Plummer, and M. Roos. “Quantification of empirical determinacy: the impact of likelihood weighting on posterior location and spread in Bayesian metaanalysis estimated with JAGS and INLA”. In: Baysian Analysis xx.xx (2022). (accepted), pp. xx–
xx. arXiv: 2109.11870 [stat.ME].

[201]

F. Lindgren, D. Bolin, and H. Rue. “The SPDE approach for Gaussian and non-Gaussian
fields: 10 years and still running”. In: Spatial Statistics xx.xx (2022). (accepted), pp. xx–xx.
DOI : https : / / doi . org / 10 . 1016 / j . spasta . 2022 . 100599. arXiv: 2111 . 01084[stat.ME].

[202]

Z. Liu and H. Rue. “Leave-Group-Out Cross-Validation For Latent Gaussian Models”. In:(submitted) xx.xx (2022), pp. xx–xx. URL: https://arxiv.org/abs/2210.04482.

[203]

J. van Niekerk and H. Rue. “Use of the INLA Approach for the Analysis of Interval-CensoredData”. In: Emerging Topics in Modeling Interval-Censored Survival Data. Ed. by Juanguo Sun andDing-Geng Chen. Springer: ICSA Book Series in Statistics, 2022, pp. 123–140.

[204]

S. M. Vandeskog, S. Martino, D. Castro-Camilo, and H. Rue. “Modelling short-term precipitation extremes with the blended generalised extreme value distribution”. In: Journal of
Agricultural, Biological, and Environmental Statistics xx.xx (2022). (accepted), pp. xx–xx. URL:
arXiv:2105.09062.

[205]

D. Yu, H. Rue, Y. Zhang, X. Wu, X. Wang, J. Qi, S. Li, and G. Li. “The spatiotemporally
varying effects of proximity to China’s high-speed rail system on county development: aBayesian spatiotemporal analysis with INLA”. In: Transportation Research Part B xx.xx (2022),
pp. xx–xx.

Publications 2021
[180]

P. de Zea Bermudez, J. M. Marín, H. Rue, and H. Veiga. “Integrated nested Laplace approximations for threshold stochastic volatility models”. In: Econometrics and Statistics xx.xx (2021).
(to appear), pp. xx–xx.

[181]

V. Gómez-Rubio, R. S. Bivand, and H. Rue. “Spatial models using Laplace approximation
methods”. In: Handbook of Regional Science. Ed. by M. M. Fisher and P. Nijkamp. 2nd andextended edition. Berlin, Germany: Springer Nature, 2021, 1943–1959n. DOI: https://doi.
org/10.1007/978-3-662-60723-7.

[182]

H. Hammer, A. Yazidi, and H. Rue. “Joint Tracking of Multiple Quantiles Through Conditional Quantiles”. In: Information Sciences xx.xx (2021). (accepted), pp. xx–xx. URL: http ://arxiv.org/abs//1902.05428.

[183]

H. L. Hammer, A. Yazidi, and H. Rue. “Estimating Tukey Depth Using Incremental Quantile
Estimator”. In: Pattern Recognition xx.xx (2021). to appear (arxiv:1911.00797), pp. xx–xx.

[184]

R. Lowe, S. A. Lee, K. M. O’Reilly, O. J. Brady, L. Bastos, G. Carrasco-Escobar, . de CastroCatão, F. J. Colón-González, C. Barcellos, M. Sá Carvalho, M. Blangiardo, H. Rue, and A.
Gasparrini. “Combined effects of hydrometeorological hazards and urbanisation on dengue
risk in Brazil: A spatiotemporal modelling study”. In: The Lancet Planetary Health 5.4 (2021),
e209–e219. ISSN: 2542-5196. DOI: https : / / doi . org / 10 . 1016 / S2542 - 5196(20 )30292 - 8. URL: https : / / www . sciencedirect . com / science / article / pii /S2542519620302928.

[185]

S. Pereira, K. F. Turkman, L. Correia, and H. Rue. “Unemployment estimation: Spatial pointreferenced methods and models”. In: Spatial Statistics 41 (2021), p. 100345. ISSN: 2211-6753.
DOI : https://doi.org/10.1016/j.spasta.2019.01.004. URL : https://www.
sciencedirect.com/science/article/pii/S2211675318301623.

[186]

M. Roos, S. Hunanyan, H. Bakka, and H. Rue. “Sensitivity and identification quantification
by a relative latent model complexity perturbation in Bayesian meta-analysis”. In: Biometric
Journal xx.xx (2021), pp. xx–xx. DOI: https://doi.org/10.1002/bimj.202000193.

[187]

S. H. Sørbye, P. G. Nicolau, and H. Rue. “Model-based bias correction for short AR(1) andAR(2) processes”. In: Statistical Inference for Stochastic Processes xx.xx (xx 2021). (accepted),
pp. xx–xx.

[188]

J. Van Niekerk, H. Bakka, and H. Rue. “A principled distance-based prior for the shape of the
Weibull model”. In: Statistics & Probability Letters xx.xx (2021). (arXiv:2002.06519, to appear),
pp. xx–xx.

[189]

J. van Niekerk, H. Bakka, and H. Rue. “Stable non-linear generalized Bayesian joint models
for survival-longitudinal data”. In: Sankhya A xx.xx (2021), pp. xx–xx. DOI: https://doi.
org/10.1007/s13171-020-00233-0.

[190]

J. van Niekerk, H. Bakka, H. Rue, and O. Schenk. “New frontiers in Bayesian modeling using
the INLA package in R”. In: Journal of Statistical Software 100.2 (2021), pp. 1–28. DOI: 10 .
18637/jss.v100.i02.

[191]

J. van Niekerk and H. Rue. “Skewed probit regression – Identifiability, contraction and reformulation”. In: REVSTAT 19.1 (Jan. 2021). https://www.ine.pt/revstat/pdf/REVSTAT_v19n1-01.pdf, pp. 1–22.

Publications 2020
[171]

P. T. Brown, C. Joshi, S. Joe, and H. Rue. “A Novel Method of Marginalisation using Low
Discrepancy Sequences for Integrated Nested Laplace Approximations”. In: Computational
Statistics & Data Analysis xx.xx (2020). (accepted, arxiv:1911.09880), pp. xx–xx.

[172]

G. A. Fuglstad, I. G. Hem, A. Knight, H. Rue, and A. Riebler. “Intuitive joint priors forvariance parameters”. In: Bayesian Analysis 15.4 (2020), pp. 1109–1137. DOI: 10.1214/19BA1185. URL: https://doi.org/10.1214/19-BA1185.

[173]

V. Gómez-Rubio, R. S. Bivand, and H. Rue. “Bayesian model averaging with the integrated
nested Laplace approximation”. In: Econometrics xx.xx (xx 2020). accepted, pp. xx–xx.

[174]

G. Konstantinoudis, D. Schuhmacher, H. Rue, and B. Spycher. “Discrete versus continuous domain models for disease mapping”. In: Spatial and Spatio-temporal Epidemiology 32.Feb(2020). DOI: https://doi.org/10.1016/j.sste.2019.100319.

[175]

T. D. Meehan, N. L. Michel, and H. Rue. “Estimating Animal Abundance with N-Mixture
Models Using the R-INLA package for R”. In: Journal of Statistical Software 95.2 (2020), p. 10016.
DOI : 10.18637/jss.v095.i02.

[176]

E. Myrvoll-Nilsen, S. H. Sørbye, H. Fredriksen, H. Rue, and M. Rypdal. “Statistical estimationof global surface temperature response to forcing under the assumption of temporal scaling”.
In: Earth System Dynamics 11.2 (2020), pp. 329–345. DOI: 10.5194/esd-11-329-2020. URL:
https://www.earth-syst-dynam.net/11/329/2020/.

[177]

S. Peluso, A. Mira, H. Rue, N. J. Tierney, C. Benvenuti, R. Cianella, M. L. Caputo, and A. Auricchio. “A Bayesian spatio-temporal statistical analysis of Out-of-Hospital Cardiac Arrests”.
In: Biometrical Journal xx.xx (2020). to appear, pp. xx–xx.

[178]

S. Pereira, K.F. Turkman, L. Correia, and H. Rue. “Spatio-temporal models for georeferenced
unemployment data”. In: Spatial Statistics 40 (2020). Space-Time Modeling of Rare Eventsand Environmental Risks: METMA Conference, p. 100363. ISSN: 2211-6753. DOI: https://doi.org/10.1016/j.spasta.2019.100363. URL: https://www.sciencedirect.
com/science/article/pii/S221167531830246X.

[179]

J. van Niekerk, H. Bakka, and H. Rue. “Competing risks joint models using R-INLA”. In:
Statistical Modelling xx.xx (2020). accepted (arXiv:1909.01637), pp. xx–xx.

Publications 2019
[158]

H. Bakka, J. Vanhatalo, J. Illian, D. Simpson, and H. Rue. “Non-stationary Gaussian models
with physical barriers”. In: Spatial Statistics 29.March (2019), pp. 268–288. DOI: https://doi.org/10.1016/j.spasta.2019.01.002.

[159]

D. Castro-Camilo, R. Huser, and H. Rue. “A spliced Gamma-Generalized Pareto model for
short-term extreme wind speed probabilistic forecasting”. In: Journal of Agricultural, Biologicaland Environmental Statistics 24.3 (2019), pp. 517–534. DOI: 10.1007/s13253-019-00369z. URL: https://doi.org/10.1007/s13253-019-00369-z.

[160]

M. Franco-Villoria, M. Ventrucci, and H. Rue. “Bayesian varying coefficient models using
PC priors”. In: Electronic Journal of Statistics 13.2 (2019), pp. 5334–5359. ISSN: 1935-7524. DOI:
10.1214/19-EJS1653.

[161]

G. Gopalan, B. Hrafnkelsson, C. K. Wikle, H. Rue, G. Aðalgeirsdóttir, A. H. Jarosch, and F.
Pálsson. “A Hierarchical Spatio-Temporal Statistical Model for Physical Systems”. In: Journalof Agricultural, Biological, and Environmental Statistics 24.4 (Dec. 2019), pp. 669–692.

[162]

H. L. Hammer, A. Yazidi, and H. Rue. “Tracking of Multiple Quantiles in Dynamically Varying Data Streams”. In: Pattern Analysis and Applications (2019). ISSN: 1433-755X. DOI: 10 .
1007 / s10044 - 019 - 00778 - 3. URL: https : / / doi . org / 10 . 1007 / s10044 - 019 00778-3.

[163]

A. Lenzi, S. Castruccio, H. Rue, and M. Genton. “Improving Bayesian Local Spatial Models in
Large Data Sets”. In: Journal of Computational and Graphical Statistics xx.xx (2019). (in revision)
arXiv:1907.06932, pp. xx–xx.

[164]

T. D. Meehan, N. L. Michel, and H. Rue. “Spatial modeling of Audubon Christmas Bird
Counts reveals fine-scale patterns and drivers of relative abundance trends”. In: Ecosphere10.4 (2019). Article e02707. DOI: 10.1002/ecs2.2707.

[165]

M. Nadifar, H. Baghishani, A. Fallah, and H. Rue. “Statistical modeling of groundwater
quality assessment in Iran using a flexible Poisson likelihood”. In: () xx.xx (2019). submitted (arXiv:1908.02344), pp. xx–xx.

[166]

T. Padellini and H. Rue. “Model-aware Quantile Regression for Discrete Data”. In: arXiv:1804.03714(2019). (submitted).

[167]

K. Seppä, H. Rue, T. Hakulinen, E. Läärä, M. J. Sillanpää, and J. Pitkäniemi. “Estimating
multilevel regional variation in excess mortality of cancer patients using integrated nested
Laplace approximation”. In: Statistics in Medicine 38.5 (2019), pp. 778–791. DOI: https://doi.org/10.1002/sim.8010.

[168]

S. H. Sørbye, J. B. Illian, D. P. Simpson, D. Burslem, and H. Rue. “Careful prior specification
avoids incautious inference for log-Gaussian Cox point processes”. In: Journal of the Royal
Statistical Society, Series C 68.3 (2019), pp. 543–564. DOI: 10.1111/rssc.12321.

[169]

S. H. Sørbye, E. Myrvoll-Nilsen, and H. Rue. “An approximate fractional Gaussian noisemodel with O(n) computational cost”. In: Statistics and Computing 29.4 (2019), pp. 821–833.
DOI : 10.1007/s11222- 018- 9843- 1. URL : https://doi.org/10.1007/s11222018-9843-1.

[170]

Y. R. Yue, D. Bolin, H. Rue, and X. Wang. “Bayesian Generalized Two-way ANOVA Modelingfor Functional Data Using INLA”. In: Statistica Sinica 29.2 (2019), pp. 741–767. DOI: https://doi.org/10.5705/ss.202016.0055.

Publications 2018
[149]

H. Bakka, H. Rue, G. A. Fuglstad, A. Riebler, D. Bolin, J. Illian, E. Krainski, D. Simpson, and
F. Lindgren. “Spatial modelling with R-INLA: A review”. In: WIREs Computational Statistics
10:e1443.6 (2018). (Invited extended review). DOI: 10.1002/wics.1443.

[150]

H. C. Bakka, D. Castro-Camilo, M. Franco-Villoria, A. Freni-Sterrantino, R. Huser, T. Opitz,
and H. Rue. “Discussion contribution to the Paper by Yao, Vehtari, Simpson and Gelman: Using Stacking to Average Bayesian Predictive Distributions”. In: Bayesian Analysis 13.3 (2018),
pp. 982–985.

[151]

A. Freni-Sterrantino, M. Ventrucci, and H. Rue. “A note on intrinsic Conditional Autoregressive models for disconnected graphs”. In: Spatial and Spatio-temporal Epidemiology 26.August(2018), pp. 25–34.

[152]

G. A. Fuglstad, D. Simpson, F. Lindgren, and H. Rue. “Constructing Priors that Penalize theComplexity of Gaussian Random Fields”. In: Journal of the American Statistical Association 0.0(2018), pp. 1–8. DOI: 10.1080/01621459.2017.1415907.

[153]

V. Gómez-Rubio and H. Rue. “Markov Chain Monte Carlo with the Integrated Nested Laplace
Approximation”. In: Statistics and Computing 28.5 (2018), pp. 1033–1051. DOI: https : / /doi.org/10.1007/s11222-017-9778-y.

[154]

H. L. Hammer, A. Yazidi, and H. Rue. “A New Quantile Tracking Algorithm Using a Generalized Exponentially Weighted Average of Observations”. In: Applied Intelligence (2018). DOI:
https://doi.org/10.1007/s10489-018-1335-7.

[155]

E. T. Krainski, V. Gómez-Rubio, H. Bakka, A. Lenzi, D. Castro-Camilio, D. Simpson, F. Lindgren, and H. Rue. Advanced Spatial Modeling with Stochastic Partial Differential Equations using
R and INLA. Github version www.r-inla.org/spde-book. CRC press (Translated to Chinese2024), Dec. 2018.

[156]

T. Opitz, R. Huser, H. Bakka, and H. Rue. “INLA goes extreme: Bayesian tail regression for
the estimation of high spatio-temporal quantiles”. In: Extremes 21.3 (2018), pp. 441–462. DOI:
https://doi.org/10.1007/s10687-018-0324-x.

Publications 2017[143]

E. Ferkingstad, L. Held, and H. Rue. “Fast and accurate Bayesian model criticism and conflict
diagnostics using R-INLA”. In: Stat 6.1 (2017), pp. 331–344. ISSN: 2049-1573. DOI: 10.1002/
sta4.163. URL: http://dx.doi.org/10.1002/sta4.163.

[144]

J. Guo, A. Riebler, and H. Rue. “Bayesian bivariate meta-analysis of diagnostic test studies
with interpretable priors”. In: Statistics in Medicine 36.19 (2017), pp. 3039–3058.

[145]

H. Rue, A. Riebler, S. H. Sørbye, J. B. Illian, D. P. Simpson, and F. K. Lindgren. “Bayesian
Computing with INLA: A Review”. In: Annual Reviews of Statistics and Its Applications 4.March(2017), pp. 395–421. DOI: 10.1146/annurev-statistics-060116-054045.

[146]

D. P. Simpson, H. Rue, A. Riebler, T. G. Martins, and S. H. Sørbye. “Penalising model component complexity: A principled, practical approach to constructing priors (with discussion)”.
In: Statistical Science 32.1 (2017), pp. 1–28.

[148]

Y. Yuan, F. E. Bachl, D. L. Borchers, F. Lindgren, J. B. Illian, S. T. Buckland, H. Rue, andT. Gerrodette. “Point Process Models for Spatio-Temporal Distance Sampling Data from a
Large-scale Survey of Blue Whales”. In: Annals of Applied Statistics 11.4 (2017), pp. 2270–2297.

Publications 2016
[137]

C. Bauer, J. Wakefield, H. Rue, S. Self, Z. Feng, and Y. Wang. “Bayesian Penalized Spline Models for the Analysis of Spatio-Temporal Count Data”. In: Statistics in Medicine 35.11 (2016),
pp. 1848–1865. DOI: 10.1002/sim.6785.

[138]

A. Riebler, S. H. Sørbye, D. Simpson, and H. Rue. “An intuitive Bayesian spatial model for
disease mapping that accounts for scaling”. In: Statistical Methods in Medical Research 25.4
(2016), pp. 1145–1165.

[139]

D. Simpson, J. Illian, F. Lindgren, S. Sørbye, and H. Rue. “Going off grid: Computational
efficient inference for log-Gaussian Cox processes”. In: Biometrika 103.1 (2016), pp. 1–22. DOI:
10.1093/biomet/asv064.

[140]

M. Ventrucci and H. Rue. “Penalized complexity priors for degrees of freedom in BayesianP-splines”. In: Statistical Modelling 16.6 (2016), pp. 429–453.

[141]

Y. Yuan, S. T. Buckland, J. B. Illian, H. Rue, and P. G. Fernandes. “Quantifying temporal
turnover in the diversity of the North Sea demersal fish community and how it varies spatially”. In: TBD xx.xx (2016). (submitted), pp. xx–xx.

[142]

H. Zareifard, H. Rue, M. J. Khaledi, and F. Lindgren. “A skew Gaussian decomposable graphical model”. In: Journal of Multivariate Analysis 145.March (2016), pp. 58–72.

Publications 2015
[127]

R. S. Bivand, V. Gómez-Rubio, and H. Rue. “Spatial Data Analysis with R-INLA with Some
Extensions”. In: Journal of Statistical Software 63.20 (2015), pp. 1–31.

[128]

E. Ferkingstad and H. Rue. “Improving the INLA approach for approximate Bayesian inference for latent Gaussian models”. In: Electronic Journal of Statistics 9 (2015), pp. 2706–2731.
DOI : 10.1214/15-EJS1092.

[129]

G. A. Fuglstad, F. Lindgren, D. Simpson, and H. Rue. “Exploring a New Class of Nonstationary Spatial Gaussian Random Fields with Varying Local Anisotropy”. In: Statistica
Sinica 25.1 (2015). Special issue of Spatial and Temporal Data Analysis, pp. 115–133.

[130]

G. A. Fuglstad, D. Simpson, F. Lindgren, and H. Rue. “Does non-stationary spatial data always require non-stationary random fields?” In: Spatial Statistics 14, Part C (2015), pp. 505–
531.

[131]

V. Gómez-Rubio, R. Bivand, and H. Rue. “A New Latent Class to Fit Spatial Econometrics
Models With Integrated Nested Laplace Approximations”. In: Procedia Envionmental Sciences
27 (2015), pp. 116–118.

[132]

F. Lindgren and H. Rue. “Bayesian Spatial Modelling with R-INLA”. In: Journal of Statistical
Software 63.19 (2015), pp. 1–25.

[133]

S. Muff, A. Riebler, H. Rue, P. Saner, and L. Held. “Bayesian analysis of measurement error
models using integrated nested Laplace approximations”. In: Journal of the Royal StatisticalSociety, Series C 64.2 (2015), pp. 231–252.

[134]

Z. Quiroz, M. O. Prates, and H. Rue. “A Bayesian Approach to estimate the biomass of anchovies in the coast of Perú”. In: Biometrics 71.1 (2015), pp. 208–217.

[135]

M. Roos, T. G. Martins, L. Held, and H. Rue. “Sensitivity analysis for Bayesian hierarchicalmodels”. In: Bayesian Analysis 10.2 (2015), pp. 321–349.

[136]

D. Simpson, F. Lindgren, and H. Rue. “Beyond the Valley of the Covariance Function”. In:
Statistical Science 30.2 (2015). (Discussion contribution to the article "Cross-Covariance Functions for Multivariate Geostatistics" by M. G. Genton and W. Kleiber), pp. 164–166.

Publications 2014
[121]

R. S. Bivand, V. Gómez-Rubio, and H. Rue. “Approximate Bayesian Inference for Spatial
Econometrics Models”. In: Spatial Statistics 9 (2014), pp. 146–165.

[122]

V. Gómez-Rubio, R. S. Bivand, and H. Rue. “Spatial Models Using Laplace Approximation
Methods”. In: Handbook of Regional Science. Ed. by M. Fischer and P. Nijkamp. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 1401–1417.

[123]

H. Jiang, P. E. Brown, H. Rue, and S. Shimakura. “Geostatistical survival models for environmental risk assessment with large retrospective cohorts”. In: Journal of the Royal StatisticalSociety, Series A 177.3 (2014), pp. 679–695.

[124]

T. G. Martins and H. Rue. “Extending INLA to a class of near-Gaussian latent models”. In:
Scandinavian Journal of Statistics 41.4 (2014), pp. 893–912.

[125]

S. H. Sørbye and H. Rue. “Scaling intrinsic Gaussian Markov random field priors in spatialmodelling”. In: Spatial Statistics 8.3 (2014), pp. 39–51.

[126]

Y. R. Yue, D. Simpson, F. Lindgren, and H. Rue. “Bayesian Adaptive Smoothing Spline using
Stochastic Differential Equations”. In: Bayesian Analysis 9.2 (2014), pp. 397–424.

Publications 2013
[110]

A. Alvaro-Meca, R. Akerkar, M. Alvarez-Bartolome, R. Gil-Prieto, H. Rue, and A. Gil deMiguel. “Factors Involved in Health Related Transitions After Curative Resection for Pancreatic Cancer 10 Year Experience: A Multi State Model”. In: Cancer Epidemiology 37.1 (2013),
pp. 91–96.

[111]

M. Blangiardo, M. Cameletti, G. Baio, and H. Rue. “Spatial and Spatio-temporal models with
R-INLA”. In: Spatial and Spatio-Temporal Epidemiology 3.December (2013), pp. 39–55.

[112]

M. Cameletti, F. Lindgren, D. Simpson, and H. Rue. “Spatio-temporal modeling of particulatematter concentration through the SPDE approach”. In: Advances in Statistical Analysis 97.2
(2013), pp. 109–131.

[113]

X. Hu, F. Lindgren, D. Simpson, and H. Rue. Multivariate Gaussian Random Fields with Oscillating Covariance Functions using Systems of Stochastic Partial Differential Equations. arXiv:1307.1384.
Department of mathematical sciences, NTNU, Norway, 2013.

[114]

X. Hu, D. Simpson, F. Lindgren, and H. Rue. Multivariate Gaussian Random Fields Using Systems of Stochastic Partial Differential Equations. arXiv:1307.1379. Department of mathematicalsciences, NTNU, Norway, 2013.

[115]

X. Hu, D. Simpson, and H. Rue. Specifying Gaussian Markov Random Fields with Incomplete
Orthogonal Factorization using Givens Rotations. arXiv:1307.1368. Department of mathematicalsciences, NTNU, Norway, 2013.

[116]

X. Hu, I. Steinsland, D. Simpson, S. Martino, and H. Rue. Spatial Modelling of Temperature and
Humidity using Systems of Stochastic Partial Differential Equations. arXiv:1307.1402. Departmentof mathematical sciences, NTNU, Norway, 2013.

[117]

T. G. Martins and H. Rue. Prior for flexibility parameters: the Student’s t case. Technical report
S8-2013. Department of mathematical sciences, NTNU, Norway, 2013.

[118]

T. G. Martins, D. Simpson, F. Lindgren, and H. Rue. “Bayesian computing with INLA: Newfeatures”. In: Computational Statistics & Data Analysis 67 (2013), pp. 68–83.

[119]

L. Serra, M. Saez ad J. Mateu, D. Varga, P. Juan, C. Dias-Ávalos, and H. Rue. “Spatio-temporal
log-Gaussian Cox processes for modelling wildfire occurrence: the case of Catalonia, 19942008”. In: Environmental and Ecological Statistics 21.3 (2013), pp. 531–563.

[120]

M. A. Van De Wiel, G. G. R. Leday, L. Pardo, H. Rue, A. W. van der Vaart, and W. N. van
Wieringen. “Bayesian analysis of high-dimensional RNA sequencing data: estimating priors
for shrinkage and multiplicity correction”. In: Biostatistics 14.1 (2013), pp. 113–128.

Publications 2012
[95]

R. Akerkar, S. Martino, and H. Rue. Approximate Bayesian inference for nonhomogeneous Poissonprocesses with application to survival analysis. Technical report 3. Department of mathematicalsciences, Norwegian University of Science and Technology, 2012.

[96]

H. Baghishani, H. Rue, and M. Mohammadzadeh. “On a Hybrid Data Cloning Method and
Its Application in Generalized Linear Mixed Models”. In: Statistics and Computing 22.2 (2012),
pp. 597–613.

[97]

J. Beguin, S. Martino, and H. Rue. “Hierarchical analysis of spatially autocorrelated ecological data using integrated nested Laplace approximation”. In: Methods in Ecology and Evolution
3.5 (2012), pp. 921–929.

[98]D. V. Divine, F. Godtliebsen, and H. Rue. “A modelling approach to assessing the timescale
uncertainties in proxy series with chronological errors”. In: Climate of the Past 8 (2012), pp. 31–
61.

[99]

J. Eidsvik, A. O. Finley, S. Banerjee, and H. Rue. “Approximate Bayesian inference for large
spatial datasets using predictive process models”. In: Computational Statistics & Data Analysis
56.6 (2012), pp. 1362–1380.

[100]

J. B. Illian, S. Sørbye, and H. Rue. “A toolbox for fitting complex spatial point process models
using integrated nested Laplace approximation (INLA)”. In: Annals of Applied Statistics 6.4(2012), pp. 1499–1530.

[101]

J. B. Illian, S. H. Sørbye, H. Rue, and D. K. Hendrichsen. “Using INLA To Fit A Complex Point
Process Model With Temporally Varying Effects – A Case Study”. In: Journal of Environmental
Statistics 3.7 (2012).

[102]

Y. Li, P. Brown, H. Rue, and D. C. Gesink. “Log Gaussian Cox Processes and spatially aggregated disease incidence data”. In: Statistical Methods in Medical Research 21.5 (2012), pp. 479–
507.

[103]

Y. Li, P. Brown, H. Rue, M. al-Maini, and P. Fortin. “Spatial modelling of Lupus incidence
over 40 years with changes in census areas”. In: Journal of the Royal Statistical Society, Series C
61 (2012), pp. 99–115.

[104]

A. Riebler, L. Held, and H. Rue. “Estimation and extrapolation of time trends in registrydata - Borrowing strength from related populations”. In: Annals of Applied Statistics 6.1 (2012),
pp. 304–333.

[105]

A. Riebler, L. Held, H. Rue, and M. Bopp. “Gender-specific differences and the impact offamily integration on time trends in age-stratified Swiss suicide rates”. In: Journal of the Royal
Statistical Society, Series A 175.2 (2012), pp. 473–490.

[106]

R. Ruiz-Cárdenas, E. T. Krainski, and H. Rue. “Direct fitting of dynamic models using integrated nested Laplace approximations - INLA”. In: Computational Statistics & Data Analysis
56.6 (2012), pp. 1808–1828.

[107]

B. Schrödle, L. Held, and H. Rue. “Assessing the impact of network data on the spatiotemporal spread of infectious diseases”. In: Biometrics 68.3 (2012), pp. 736–744.

[108]

D. Simpson, F. Lindgren, and H. Rue. “In order to make spatial statistics computationallyfeasible, we need to forget about the covariance function”. In: Environmetrics 23.1 (2012),
pp. 65–74.

[109]

K. Thon, H. Rue, S. O. Skøvseth, and F. Godtliebsen. “Bayesian multiscale analysis of images
modeled as Gaussian Markov random fields”. In: Computational Statistics & Data Analysis
56.1 (2012), pp. 49–61.

Publications 2011[87]

T. Guerrera, H. Rue, and D. Simpson. “Comment on Riemann manifold Langevin and Hamiltonian Monte Carlo methods " by M. Girolami and B. Calderhead”. In: Journal of the Royal
Statistical Society, Series B 73 (2011), p. 184.

[88]

F. Lindgren, T. Martins, H. Rue, and D. Simpson. “Discussion on "Spatial prediction in thepresence of positional error" by T. R. Fanshawe and P. J. Diggle”. In: Environmetrics 22.2
(2011), p. 127.

[89]

F. Lindgren, H. Rue, and J. Lindström. “An explicit link between Gaussian fields and Gaussian Markov random fields: The SPDE approach (with discussion)”. In: Journal of the Royal
Statistical Society, Series B 73.4 (2011), pp. 423–498.

[90]D. Simpson, F. Lindgren, and H. Rue. Fast approximate inference with INLA: the past, the present
and the future. Tech. rep. arXiv.org, 2011.

[91]

D. P. Simpson, F. K. Lindgren, and H. Rue. “Think continous: Markovian Gaussian models
in spatial statistics”. In: Spatial Statistics 1.1 (2011), pp. 16–29.

[92]

S. H. Sørbye and H. Rue. “Simultanous credible bands for latent Gaussian models”. In: Scandinavian Journal of Statistics 38.4 (2011), pp. 712–725.

[93]

J. Wyse, N. Friel, and H. Rue. “Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments (with discussion)”. In: Bayesian
Analysis 6.4 (2011), pp. 501–546.

[94]

Y. Yue and H. Rue. “Bayesian inference for structured additive quantile regression models”.
In: Computational Statistics & Data Analysis 55.1 (2011), pp. 84–96.

Publications 2010[76]

Y. Fong, H. Rue, and J. Wakefield. “Bayesian inference for Generalized Linear Mixed Models”. In: Biostatistics 11.3 (2010), pp. 397–412.

[77]

L. Held and H. Rue. “Conditional and intrinsic autoregressions”. In: Handbook of Spatial
Statistics. Ed. by A. Gelfand, P. Diggle, M. Fuentes, and P. Guttorp. Boca Raton, FL: CRC/Chapman& Hall, 2010, pp. 201–216.

[78]

L. Held, B. Schrödle, and H. Rue. “Posterior and cross-validatory predictive checks: A comparison of MCMC and INLA”. In: Statistical Modelling and Regression Structures – Festschrift in
Honour of Ludwig Fahrmeir. Ed. by T. Kneib and G. Tutz. Berlin: Springer Verlag, 2010, pp. 91–
110.

[79]

P. Jacob, N. Chopin, C. P. Robert, and H. Rue. “Discussion on the Paper by Andrieu, Doucet
and Holenstein: Particle Markov chain Monte Carlo methods”. In: Journal of the Royal Statistical Society, Series B 72.3 (2010), pp. 323–324.

[80]

S. Martino, K. Aas, O. Lindqvist, L. R. Neef, and H. Rue. “Estimating stochastic volatility
models using integrated nested Laplace approximation”. In: The European Journal of Finance(2010), pp. 1–17.

[81]

S. Martino, R. Akerkar, and H. Rue. “Approximate Bayesian Inference for Survival Models”.
In: Scandinavian Journal of Statistics 28.3 (2010), pp. 514–528.

[82]

S. Martino and H. Rue. “Case Studies in Bayesian Computation using INLA”. In: Complex
Data Modeling and Computationally Intensive Statistical Methods. Ed. by P. Mantovan and P.
Secchi. Contributions to Statistics. Springer-Verlag Italia, 2010, pp. 99–114.

[83]

M. Paul, A. Riebler, L. M. Bachmann, H. Rue, and L. Held. “Bayesian bivariate meta-analysis
of diagnostic test studies using integrated nested Laplace approximations”. In: Statistics in
Medicine 29.12 (2010), pp. 1325–1339.

[84]

C. P. Robert, N. Chopin, and H. Rue. “Discussion on the Paper by Andrieu, Doucet andHolenstein: Particle Markov chain Monte Carlo methods”. In: Journal of the Royal StatisticalSociety, Series B 72.3 (2010), pp. 311–314.

[85]

H. Rue and L. Held. “Markov random fields”. In: Handbook of Spatial Statistics. Ed. by A.
Gelfand, P. Diggle, M. Fuentes, and P. Guttorp. Boca Raton, FL: CRC/Chapman & Hall, 2010,
pp. 171–200.

[86]

H. Rue, S. Martino, D. Mondal, and N. Chopin. “Discussion on the Paper by Diggle, Menezes
and Su: Geostatistical inference under preferential sampling”. In: Journal of the Royal Statistical Society, Series C 59.2 (2010), pp. 221–222.

Publications 2009[71]

J. Eidsvik, S. Martino, and H. Rue. “Approximate Bayesian inference in spatial generalized
linear mixed models”. In: Scandinavian Journal of Statistics 36.1 (2009), pp. 1–22.

[72]

S. Frühwirth-Schnatterr, R. Frühwirth-Schnatter, L. Held, and H. Rue. “Improved Auxiliary
Mixture Sampling for Hierarchical Models of Non-Gaussian Data”. In: Statistics and Computing 19.4 (2009), pp. 479–492.

[73]

S. Hovda, H. Rue, and B. Olstad. “New Doppler-Based Imaging Method in Echocardiaography with Applications in Blood/Tissue Segmentation”. In: Computer Methods and Programsin Biomedicine 96 (1 2009), pp. 12–24.

[74]

H. Rue, S. Martino, and N. Chopin. “Approximate Bayesian Inference for Latent Gaussian
Models Using Integrated Nested Laplace Approximations (with discussion)”. In: Journal of
the Royal Statistical Society, Series B 71.2 (2009), pp. 319–392.

[75]

S. H. Sørbye, K. Hindberg, L. R. Olsen, and H. Rue. “Bayesian multiscale feature detection oflog-spectral densities”. In: Computational Statistics & Data Analysis 53 (2009), pp. 3746–3754.

[237]

D. Myrhaug and H. Rue. “On a joint distribution of two successive surf parameters”. In:
Coastal Processes. Ed. by C. A. Brebbia, G. Benassai, and G. R. Rodriguez. WIT Transactions on
Ecology and the Environment 126. WIT Press, Southampton, Boston, 2009. DOI: 10.2495/
CP090081.

Publications 2008
[66]

A. Benini, T. Owen, and H. Rue. “A Semi-Parametric Spatial Regression Approach to PostWar Human Security: Cambodia, 2002-2004”. In: Asian Journal of Criminology 3.2 (2008), pp. 139–
158. DOI: 10.1007/s11417-008-9056-1.

[67]

E. Ferkingstad, A. Frigessi, H. Rue, G. Thorleifsson, and A. Kong. “Unsupervised Empirical
Bayesian Multiple Testing with External Covariates”. In: Annals of Applied Statistics 2.2 (2008),
pp. 714–735.

[68]

S. Hovda, H. Rue, and B. Olstad. “Bandwidth of the Ultrasound Doppler Signal with Applications in Blood/Tissue Segmentation in the Left Ventricle”. In: Medical Imaging and Informatics. Ed. by X. Gaoo, H. Müller, M. Loomes, R. Comley, and S. Luo. Revised Selected Papers
from the 2nd International Conference, MIMI 2007, Beijing, China, August 2007. Springer,
2008, pp. 207–215.

[69]

Sigve Hovda, Havard Rue, and Bjorn Olstad. “New echocardiographic imaging method
based on the bandwidth of the ultrasound Doppler signal with applications in blood/tissue
segmentation in the left ventricle”. In: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
92.3, Sp. Iss. SI (DEC 2008), 279–288. DOI: 10.1016/j.cmpb.2008.03.002.

[70]

F. Lindgren and H. Rue. “A note on the second order random walk model for irregular locations”. In: Scandinavian Journal of Statistics 35.4 (2008), pp. 691–700.

Publications 2007
[63]

N. Friel and H. Rue. “Recursive computing and simulation-free inference for general factorizable models”. In: Biometrika 94.3 (2007), pp. 661–672.

[64]

D. Myrhaug, H. Føien, and H. Rue. “Tentative Engineering approach to scour around spherical bodies in random waves”. In: Applied Ocean Research 29 (2007), pp. 80–85.

[65]H. Rue and S. Martino. “Approximate Bayesian Inference for Hierarchical Gaussian Markov
Random Fields Models”. In: Journal of Statistical Planning and Inference 137.10 (2007). Special
Issue: Bayesian Inference for Stochastic Processes, pp. 3177–3192.

Publications 2006
[57]

L. Held, G. Graziano, C. Frank, and H. Rue. “A joint spatial analysis of gastrointestinal infectious diseases”. In: Statistical Methods in Medical Research 15.5 (2006), pp. 465–480.

[58]

S. Kolberg, H. Rue, and L. Gottschalk. “A Bayesian spatial assimilation scheme for snow
coverage observations in a gridded snow model”. In: Hydrology and Earth System Sciences 3(2006), pp. 369–381.

[59]

D. Myrhaug, L. E. Holmedal, and H. Rue. “Erosion and Deposition of Mud beneath Random
Waves”. In: Coastal Engineering 53.9 (2006), pp. 793–797.

[60]

T. A. Øigård, H. Rue, and F. Godtliebsen. “Bayesian multiscale analysis for time series data”.
In: Computational Statistics & Data Analysis 51.3 (2006), pp. 1719–1730.

[61]

H. T. Wist, D. Myrhaug, and H. Rue. “Second order model for wave crest used in predictionof green water load and volume on ships in random waves”. In: Journal of Offshore Mechanics
and Artic Engineering 128.11 (2006), pp. 271–275.

[62]

H. T. Wist and H. Rue. “Specifying a Gaussian Markov random field by a sparse Cholesky
triangle”. In: Communications in Statistics: Simulation and Computation 35.1 (2006), pp. 161–176.

Publications 2005
[52]

L. Held, I. Natario, S. Fenton, H. Rue, and N. Becker. “Towards joint disease mapping”. In:
Statistical Methods in Medical Research 14.1 (2005), pp. 61–82.

[53]

D. Myrhaug, L. E. Holmedal, and H. Rue. “Bottom friction and bedload sediment transportcaused by boundary layer streaming beneath random waves”. In: Applied Ocean Research 26.5
(2005), pp. 183–197.

[54]

D. Myrhaug and H. Rue. “Scour around group of slender vertical piles in random waves”.
In: Applied Ocean Research 27.1 (2005), pp. 56–63.

[55]

H. Rue and L. Held. Gaussian Markov Random Fields: Theory and Applications. Vol. 104. Monographs on Statistics and Applied Probability. London: Chapman & Hall, 2005.

[56]

H. T. Wist, D. Myrhaug, and H. Rue. “Statistical properties of successive wave heights andsuccessive wave periods”. In: Applied Ocean Research 26.3-4 (2005), pp. 114–136.

Publications 2004
[47]

O. K. Husby and H. Rue. “Estimating blood vessel areas in ultrasound images using a deformable template model”. In: Statistical modelling 4.3 (2004), pp. 211–226.

[48]

D. Myrhaug, L. E. Holmedal, and H. Rue. “Bottom friction and bedload sediment transportcaused by boundary layer streaming beneath random waves”. In: Applied Ocean Research 26(2004). (printed in 2005), pp. 183–197.

[49]

D. Myrhaug, H. Rue, and A. Tørum. “Tentative engineering approach to scour around breakwaters in random waves”. In: Coastal Engineering 51.10 (2004), pp. 1051–1065.

[50]

H. Rue. “2D Object Detection and Recognition: Models, Algorithms, and Networks. (Book
Review)”. In: Journal of the American Statistical Association 99.467 (2004), pp. 902–903.

[51]H. Rue, I. Steinsland, and S. Erland. “Approximating hidden Gaussian Markov random
fields”. In: Journal of the Royal Statistical Society, Series B 66.4 (2004), pp. 877–892.

Publications 2003
[41]

T. Follestad and H. Rue. Modelling spatial variation in disease risk using Gaussian Markov random
field proxies for Gaussian random fields. Statistics Report No. 3. Department of Mathematical
Sciences, Norwegian University of Science and Technology, Trondheim, Norway, 2003.

[42]

D. Gamerman, A. R. B. Moreira, and H. Rue. “Space-varying regression models: Specifications and simulations”. In: Computational Statistics & Data Analysis 42.3 (2003), pp. 513–533.

[43]

L. E. Holmedal, D. Myrhaug, and H. Rue. “The sea bed boundary layer under random waves
plus current”. In: Continental Shelf Research 23 (2003), pp. 717–750.

[44]

M. A. Hurn, O. K. Husby, and H. Rue. “A tutorial on image analysis”. In: Spatial Statistics
and Computational Methods. Ed. by J. Møller. Lecture Notes in Statistics; 173. Berlin: SpringerVerlag, 2003, pp. 87–141.

[45]

M. A. Hurn, O. K. Husby, and H. Rue. “Advances in Bayesian image analysis”. In: Highly
Structured Stochastic Systems. Ed. by P. J. Green, N. L. Hjort, and S. Richardson. Oxford Statistical Science Series, no 27. Oxford University Press, 2003, pp. 301–322.

[46]

D. Myrhaug and H. Rue. “Scour below pipelines and around vertical piles in random waves”.
In: Coastal Engineering 48.4 (2003), pp. 227–242.

Publications 2002
[35]

A. Frigessi, O. Haug, and H. Rue. “Tail estimation with the Generalized Pareto distribution
without threshold selction”. In: Extremes 5 (2002), pp. 219–235.

[36]

L. Knorr-Held and H. Rue. “On block updating in Markov random field models for disease
mapping”. In: Scandinavian Journal of Statistics 29.4 (2002), pp. 597–614.

[37]

D. Myrhaug, H. T. Wist, and H. Rue. “Joint distributions of successive wave crest heights
and successive wave through depths for nonlinear waves”. In: Journal of Ship Research 46.3
(2002), pp. 175–185.

[38]

H. Rue, C. H. Chu, F. Godtliebsen, and J. S. Marron. “M-smoother with local linear fit”. In:
Journal of Nonparametric Statistics 14.1-2 (2002), pp. 155–168.

[39]

H. Rue and H. Tjelmeland. “Fitting Gaussian Markov random fields to Gaussian fields”. In:
Scandinavian Journal of Statistics 29.1 (2002), pp. 31–50.

[40]

H. T. Wist, D. Myrhaug, and H. Rue. “Joint Distributions of Successive Wave Crest Heights
and Successive Wave Trough Depths for Second Order Nonlinear Waves”. In: Journal of Ship
Research 46.3 (2002), pp. 175–185.

Publications 2001
[29]

P. A. Brodtkorb, D. Myrhaug, and H. Rue. “Joint distribution of wave height and wave crest
velocity from reconstructed data”. In: International Journal of Offshore and Polar Engineering
11.1 (2001), pp. 23–32.

[30]

P. A. Brodtkorb, D. Myrhaug, and H. Rue. “Joint Distribution of Wave Height and Wave Crest
Velocity from Reconstructed Data with Application to Ringing”. In: International Journal of
Offshore and Polar Engineering 11.1 (2001), pp. 23–32.

[31]

M. A. Hurn, I. Steinsland, and H. Rue. “Parameter estimation for a deformable templatemodel”. In: Statistics and Computing 11.4 (2001), pp. 337–346.

[32]

O. K. Husby, T. Lie, T. Langø, J. Hokland, and H. Rue. “Bayesian 2D deconvolution: A modelfor diffuse ultrasound scattering”. In: IEEE Transaction of Ultrasonic Ferroelectric Frequency andControl 48.1 (2001), pp. 121–130.

[33]

O. K. Husby and H. Rue. Estimating blood vessel areas in ultrasound imaging using a deformabletemplate model. Statistics Report No. 16. Department of Mathematical Sciences, Norwegian
University of Science and Technology, Trondheim, Norway, 2001.

[34]

H. Rue. “Fast sampling of Gaussian Markov Random Fields”. In: Journal of the Royal StatisticalSociety, Series B 63.2 (2001), pp. 325–338.

Publications 2000
[24]

A. Frigessi, J. Gåsemyr, and H. Rue. “Antithetic coupling of two Gibbs sampler chains”. In:
The Annals of Statistics 28.4 (2000), pp. 1128–1149.

[25]

L. E. Holmedal, D. Myrhaug, and H. Rue. “Seabed shear stresses under irregular waves plus
current from Monte Carlo simulations of parameterized models”. In: Coastal Engineering 39(2000), pp. 123–147.

[26]

D. Myrhaug, E. A. Dahle, H. Rue, and O. H. Slaattelid. “Statistics of successive wave periods with application to rolling of ships”. In: International Shipbuilding Progress 47.451 (2000),
pp. 253–266.

[27]

D. Myrhaug, E. Aall Dahle, H. Rue, and O. H. Slaattelid. “Statistics of Successive Wave Periods with Application to Rolling of Ships”. In: International Shipbuilding Progress 47.451 (2000),
pp. 253–266.

[28]

H. Rue and Ø. Salvesen. “Prediction and retrospective analysis of soccer matches in a league”.
In: The Statistician 49.3 (2000), pp. 399–418.

Publications 1999
[19]

L. E. Holmedal, D. Myrhaug, and H. Rue. “Monte Carlo simulations of seabed shear stresses
under irregular waves plus current”. In: Computational Stochastic Mechanics. Ed. by P. D.
Spanos. Proceedings from the third international conference on computational stochastic
mechanics, Thera-Santorini, Greece, 14-17 June 1998. Balkema: Rotterdam, 1999, pp. 21–30.

[20]

M. A. Hurn, H. Rue, and N. A. Sheehan. “Block updating in constrained Markov chain Monte
Carlo sampling”. In: Statistics & Probability Letters 41.4 (1999), pp. 353–361.

[21]

H. Rue. “Baddeley’s delta metric”. In: Encyclopedia of Statistical Sciences. Ed. by S. Kotz, C. B.
Read, and D. L Banks. Vol. Update Volume 3. Chichester: John Wiley & Sons, Ltd., 1999,
pp. 158–162.

[22]

H. Rue and M. A. Hurn. “Bayesian object identification”. In: Biometrika 86.3 (1999), pp. 649–
660.

[23]

H. Rue and O. K. Husby. “Modern statistical methods: A first introduction”. In: Normat 47.1
(1999), pp. 20–33.

Publications 1998
[16]

D. Myrhaug and H. Rue. “Note on a Joint Distribution of Successive Wave Periods Revisited”. In: Journal of Ship Research 42.3 (1998), pp. 199–206.

[17]H. Rue and O. K. Husby. “Identification of partly destroyed objects using deformable templates”. In: Statistics and Computing 8.3 (1998), pp. 221–228.

[18]

H. Rue and A. R. Syversveen. “Bayesian object recognition with Baddeley’s delta loss”. In:
Advances in Applied Probability (SGSA) 30.1 (1998), pp. 64–84.

Publications 1997
[11]

A. Frigessi and H. Rue. “Bayesian image classification with Baddeley’s delta loss”. In: Journalof Computational and Graphical Statistics 6.1 (1997), pp. 55–73.

[12]

M. A. Hurn and H. Rue. “High–level image priors in confocal microscopy applications”.
In: The Art and Science of Bayesian Image Analysis. Ed. by K. V. Mardia, C. A. Gill, and R. G.
Aykroyd. University of Leeds, 1997, pp. 36–43.

[13]

H. Rue. “A loss function model for the restoration of grey level images”. In: Scandinavian
Journal of Statistics 24.1 (1997), pp. 103–114.

[14]

H. Rue and M. A. Hurn. “Loss Functions for Bayesian Image Analysis”. In: The Art and Scienceof Bayesian Image Analysis. Ed. by K. V. Mardia, C. A. Gill, and R. G. Aykroyd. University ofLeeds, 1997, pp. 72–79.

[15]

A. R. Syversveen and H. Rue. “A Method for Approximate Fully Bayesian Analysis of Parameters”. In: Communications in Statistics: Simulation and Computation 26.3 (1997), pp. 1147–
1162.

Publications 1995
[8] F. Godtliebsen and H. Rue. “Discussion of a paper by D. Girard”. In: Computational Statistics
10.4 (1995), pp. 235–238.
[9] D. Myrhaug, E. Aa. Dahle, and H. Rue. “A Two-dimensional Weibull Distribution and its Application to Rolling”. In: Journal of Ocean Mechanics and Arctic Engineering 117 (1995), pp. 178–
182.
[10]

H. Rue. “New loss functions in Bayesian imaging”. In: Journal of the American Statistical Association 90 (1995), pp. 900–908.

Publications 1993
[3] D. Myrhaug, E. Aa. Dahle, and H. Rue. A Two–Dimensional Weibull Distribution and its Application to Rolling. Ocean Mechanics and Arctic Engineering (OMAE). Conference Paper No.
1208. 1993.
[4] D. Myrhaug and H. Rue. “Joint distributions of successive wave steepness parameters”. In:
Journal of Offshore Mechanics and Arctic Engineering 115.3 (Aug. 1993), pp. 191–195.
[5] D. Myrhaug and H. Rue. “Note on a Joint Distribution of Successive Wave Periods”. In:
Journal of Ship Research 37.3 (1993), pp. 208–212.
[6] J. Myrheim, H. Rue, and L. Bugge. “A Simple Algorithm for Large, Nonlinear or Singular
Least Squares Problems”. In: Nuclear Instruments and Methods in Physics Research A327 (1993),
pp. 539–543.
[7] H. Rue. “Topics in Image Analysis”. PhD thesis. The Norwegian Institute of Technology,
1993.

Publications 1992[2] J. Myrheim and H. Rue. “New algorithms for maximum entropy image restoration”. In:
Graphical Models and Image Processing 54.3 (1992), pp. 223–238.

Publications 1991
[1] D. Myrhaug and H. Rue. “Simplified Model for a Weakly Unstable Atmospheric Boundary
Layer Capped by an Inversion Layer”. In: Journal of Wind Engineering and Industrial Aerodynamics 37 (1991), pp. 123–139.