The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Interpolation of Spatial Data
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Handbook of Mathematical Functions
McQuarrie, D. A. (1966) · American Journal of Physics · doi:10.1119/1.1972842 · Cited by 40,433
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Bootstrap Methods and their Application
Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350
Cited by
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Lindgren, F., Rue, H., & Lindström, J. (2011) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2011.00777.x · Cited by 2,708
Navigating the protein fitness landscape with Gaussian processes
Romero, P. A., Krause, A., & Arnold, F. H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215251110 · Cited by 360
Spatial Analysis to Quantify Numerical Model Bias and Dependence: How Many Climate Models Are There?
Jun, M., Knutti, R., & Nychka, D. W. (2008) · Journal of the American Statistical Association · doi:10.1198/016214507000001265 · Cited by 214
Explaining Neural Scaling Laws
Bahri, Y., Dyer, E., Kaplan, J., Lee, J., & Sharma, U. (2021) · arXiv · doi:10.48550/arxiv.2102.06701 · Cited by 182
Explaining neural scaling laws
Bahri, Y., Dyer, E., Kaplan, J., Lee, J., & Sharma, U. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311878121 · Cited by 181
Kernel-based whole-genome prediction of complex traits: a review
Morota, G., & Gianola, D. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00363 · Cited by 162
Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots
Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S. M., & Christensen, A. L. (2016) · PLOS ONE · doi:10.1371/journal.pone.0151834 · Cited by 134
Mapping routine measles vaccination in low- and middle-income countries
Collaborators, L. B. O. D. V. C., Sbarra, A. N., Rolfe, S., Nguyen, J. Q., Earl, L., Galles, N. C., Marks, A., Abbas, K. M., Abbasi-Kangevari, M., Abbastabar, H., et al. (2021) · Nature · doi:10.1038/s41586-020-03043-4 · Cited by 125
Alignment of spatial genomics data using deep Gaussian processes
Jones, A., Townes, F. W., Li, D., & Engelhardt, B. E. (2023) · Nature Methods · doi:10.1038/s41592-023-01972-2 · Cited by 85
Hovering efficiency optimization of the ducted propeller with weight penalty taken into account
Hu, Y., Qing, J. X., Liu, Z. H., Conrad, Z. J., Cao, J. N., & Zhang, X. P. (2021) · Aerospace Science and Technology · doi:10.1016/j.ast.2021.106937 · Cited by 84
In-plane shape-deviation modeling and compensation for fused deposition modeling processes
Wang, A., Song, S., Huang, Q., & Tsung, F. (2017) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2544941 · Cited by 79
Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings
Diggle, P. J., & Giorgi, E. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1123158 · Cited by 56
Detecting periodicities with Gaussian processes
Durrande, N., Hensman, J., Rattray, M., & Lawrence, N. D. (2016) · PeerJ Computer Science · doi:10.7717/peerj-cs.50 · Cited by 39
Hosseini, R., Newlands, N., Dean, C., & Takemura, A. (2015) · Remote Sensing · doi:10.3390/rs70302752 · Cited by 31
Tensor cubic smoothing splines in designed experiments requiring residual modelling
Verbyla, A. P., De Faveri, J., Wilkie, J. D., & Lewis, T. (2018) · Journal of Agricultural, Biological and Environmental Statistics · doi:10.1007/s13253-018-0334-9 · Cited by 16
Structural Seismic Reliability Analysis
Sankararaman, S. (2014) · Encyclopedia of Earthquake Engineering · doi:10.1007/978-3-642-36197-5_280-1 · Cited by 2
Metadata
- Authors
- Stein, M. L.
- Venue
- Springer Series in Statistics
- Publisher
- Springer New York
- Published
- 1999
- ISSN
- 0172-7397 (ISSN-L)
- OpenAlex
- W55912154
- Cited by
- 2,348
- References
- 3
- FWCI
- 25.49
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
