Submitted

• 27. T. Filosi, E. Porcu, X. Emery, C. Agostinelli & A. Alegría

Vector-Valued Gaussian Processes and their Kernels on a Class of Metric Graphs

• 26. A. Alegría, X. Emery, T. Filosi & E. Porcu 

Computationally Efficient Algorithms for Simulating Isotropic Gaussian Random Fields on Graphs with Euclidean Edges (arxiv)

• 25. A. Alegría 

Assessing the Competitiveness of Matrix-Free Block Likelihood Estimation in Spatial Models (arxiv)

Accepted/Published

• 24. A. Alegría & X. Emery (2026)

Versatile Parametric Classes of Covariance Functions that Interlace Anisotropies and Hole Effects

Statistica Sinica (forthcoming)

• 23. A. Alegría, G. Cleanthous, A. Georgiadis, E. Porcu & P. White (2024)

Gaussian Random Fields on the Product of Spheres: Theory and Applications

Electronic Journal of Statistics, 18(1), 1394-1435

• 22. A. Alegría & X. Emery (2024) 

Matrix-Valued Isotropic Covariance Functions with Local Extrema

Journal of Multivariate Analysis, 200, 105250

• 21. A. Alegría, F. Ramírez & E. Porcu (2024+) 

Hybrid Parametric Classes of Isotropic Covariance Functions for Spatial Random Fields 

    Mathematical Geosciences (forthcoming)

• 20. A. Alegría (2023)

Effective Sample Size for Georeferenced and Temporally Evolving Data

Spatial Statistics, 53, 100721

• 19. X. Emery & A. Alegría (2022)

The Gauss Hypergeometric Covariance Kernel for Modeling Second-Order Stationary Random Fields in Euclidean Spaces: its Compact Support, Properties and Spectral Representation

Stochastic Environmental Research and Risk Assessment, 36(9), 2819-2834

• 18. X. Emery, A. Alegría & D. Arroyo (2021)

Covariance models and simulation algorithm for stationary vector random fields on spheres crossed with Euclidean spaces 

SIAM Journal on Scientific Computing, 43(5), A3114–A3134

• 17. J. Acosta, A. Alegría, F. Osorio & R. Vallejos (2021) 

Assessing the effective sample size for large spatial datasets: A block likelihood approach

Computational Statistics & Data Analysis, 162, 107282

• 16. A. Alegría, F. Cuevas, P. Diggle & E. Porcu (2021) 

The F family of covariance functions: A Matérn analogue for modeling random fields on spheres

Spatial Statistics, 43, 100512

• 15. A. Alegría, P.G. Bissiri, G. Cleanthous, E. Porcu & P. White (2021)

Multivariate Isotropic Random Fields on Spheres: Nonparametric Bayesian Modeling and Lp Fast Approximations

Electronic Journal of Statistics, 15(1), 2360-2392

• 14. A. Alegría, X. Emery & E. Porcu (2021)

Bivariate Matérn Covariances with Cross-Dimple for Modeling Coregionalized Variables

Spatial Statistics, 41, 100491

• 13. X. Emery & A. Alegría (2020)

A spectral algorithm to simulate nonstationary random fields on spheres and multifractal star-shaped random sets

Stochastic Environmental Research and Risk Assessment, 34(12), 2301-2311

• 12. A. Alegría, X. Emery & C. Lantuéjoul (2020)

The turning arcs: a computationally efficient algorithm to simulate isotropic vector-valued Gaussian random fields on the d-sphere 

Statistics and Computing, 30(5), 1403-1418

• 11. A. Alegría & F. Cuevas (2020)

Karhunen-Loève expansions for axially symmetric Gaussian processes: modeling strategies and L2 approximations

Stochastic Environmental Research and Risk Assessment, 34(11), 1953-1965

• 10. A. Alegría (2020)

Cross-Dimple in the Cross-Covariance Functions of Bivariate Isotropic Random Fields on Spheres

Stat, 9(1):e301

• 9. E. Porcu, S. Castruccio, A. Alegría & P. Crippa (2019)

Axially symmetric models for global data: A journey between geostatistics and stochastic generators​ 

Environmetrics, 30(1), e2555

• 8. A. Alegría, E. Porcu, R. Furrer & J. Mateu (2019)

Covariance functions for multivariate Gaussian fields evolving temporally over planet Earth 

Stochastic Environmental Research and Risk Assessment, 33(8), 1593-1608

• 7. J. Clarke, A. Alegría & E. Porcu (2018) 

Regularity properties and simulations of Gaussian random fields on the sphere cross time

Electronic Journal of Statistics, 12(1), 399-426

• 6. E. Porcu, A. Alegría & R. Furrer (2018)

Modeling Temporally Evolving and Spatially Globally Dependent Data

International Statistical Review, 86(2), 344-377

• 5. A. Alegría, E. Porcu & R. Furrer (2018)

Asymmetric Matrix-Valued Covariances for Multivariate Random Fields on Spheres

Journal of Statistical Computation and Simulation, 88(10), 1850-1862

• 4. A. Alegría & E. Porcu (2017) 

The dimple problem related to space-time modeling under the Lagrangian framework

Journal of Multivariate Analysis, 162, 110-121

• 3. A. Alegría, S. Caro, M. Bevilacqua, E. Porcu & J. Clarke (2017)

Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere

Spatial Statistics, 22, 388-402

• 2. A. Alegría, M. Bevilacqua & E. Porcu (2016) 

Likelihood-based inference for multivariate space-time wrapped-Gaussian fields

Journal of Statistical Computation and Simulation, 86(13), 2583-2597

• 1. M. Bevilacqua, A. Alegría, D. Velandia & E. Porcu (2016)

Composite likelihood inference for multivariate Gaussian random fields

Journal of Agricultural Biological and Environmental Statistics, 21(3), 448-469