| Mitigating spatial confounding by explicitly correlating Gaussian random fields I Marques, T Kneib, N Klein Environmetrics 33 (5), e2727, 2022 | 24 | 2022 |
| Biomass allocation and leaf morphology of saplings grown under various conditions of light availability and competition types I Bebre, I Marques, P Annighöfer Plants 11 (3), 305, 2022 | 12 | 2022 |
| Non-stationary spatial regression for modelling monthly precipitation in Germany I Marques, N Klein, T Kneib Spatial Statistics 40, 100386, 2020 | 11 | 2020 |
| Demystifying spatial confounding E Dupont, I Marques, T Kneib arXiv preprint arXiv:2309.16861, 2023 | 7 | 2023 |
| UAV-based thermography reveals spatial and temporal variability of evapotranspiration from a tropical rainforest M Bulusu, F Ellsäßer, C Stiegler, J Ahongshangbam, I Marques, ... Frontiers in Forests and Global Change 6, 1232410, 2023 | 7 | 2023 |
| A multivariate Gaussian random field prior against spatial confounding I Marques, T Kneib, N Klein arXiv preprint arXiv:2106.03737, 2021 | 6 | 2021 |
| A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes I Marques, T Kneib, N Klein Statistics and Computing 32 (5), 73, 2022 | 5 | 2022 |
| Estimating the spatial distribution of the white shark in the Mediterranean Sea via an integrated species distribution model accounting for physical barriers G Panunzi, S Moro, I Marques, S Martino, F Colloca, F Ferretti, ... Environmetrics 36 (1), e2876, 2025 | 4 | 2025 |
| Discussion on “Spatial+: A novel approach to spatial confounding” by Emiko Dupont, Simon N. Wood, and Nicole H. Augustin AM Schmidt Biometrics 78 (4), 1300-1304, 2022 | 4 | 2022 |
| Bayesian spatial+: A joint model perspective I Marques, PFV Wiemann arXiv preprint arXiv:2309.05496, 2023 | 1 | 2023 |
| Mitigating spatial confounding by explicitly correlating Gaussian random fields I Marques, T Kneib, N Klein Environmetrics 34 (4), e2801, 2023 | 1 | 2023 |
| Changes in leaf area index by tropical forest transformation to plantations increase below-canopy surface temperatures A Röll, I Marques, DN Ramadhani, A Valdes-Uribe, H Hendrayanto, ... Global Ecology and Conservation 53, e03001, 2024 | | 2024 |
| Navigating Spatial Confounding in a Bayesian Framework: Assessment, Approaches, and Practical Recommendations for Researchers I Marques, E Dupont, T Kneib, P Wiemann 2023 IMS International Conference on Statistics and Data Science (ICSDS), 128, 2023 | | 2023 |
| A Variance Partitioning Multi-level Model for Forest Inventory Data with a Fixed Plot Design I Marques, PFV Wiemann, T Kneib Journal of Agricultural, Biological and Environmental Statistics 28 (4), 706-725, 2023 | | 2023 |
| Biomass Allocation and Leaf Morphology of Saplings Grown under Various Conditions of Light Availability and Competition Types. Plants 2022, 11, 305 I Bebre, I Marques, P Annighöfer s Note: MDPI stays neutral with regard to jurisdictional claims in published …, 2022 | | 2022 |
| Recent Advances in Continuous Space Spatial Statistics: From Non-stationarity to Spatial Confounding IMRG Marques Georg-August-Universität Göttingen, 2021 | | 2021 |
| Introducing non-stationarity to wrappedgaussian spatial responses with anapplication to wind direction N Klein, T Kneib, I Marques Proceedings of the 35th International Workshop on Statistical Modelling …, 2020 | | 2020 |
| Changes in Leaf Area Index by Land Transformation Drive Below-Canopy Surface Temperature in a Tropical Mosaic Landscape P Pallavi, A Röll, I Marques, DN Ramadhani, A Valdes-Uribe, ... Available at SSRN 4680349, 0 | | |
| Non-stationary wrapped Gaussian spatial response model I Marques, T Kneib, N Klein | | |
| Spatial multi-resolution models for small forestry data sets I Marques, PFV Wiemann, T Kneib METMA X, 253, 0 | | |
