The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
On p-values for smooth components of an extended generalized additive model
Open accessAbstract
The problem of testing smooth components of an extended generalized additive model for equality to zero is considered. Confidence intervals for such components exhibit good across-the-function coverage probabilities if based on the approximate result , where f is the vector of evaluated values for the smooth component of interest and Vf is the covariance matrix for f according to the Bayesian view of the smoothing process. Based on this result, a Wald-type test of f=0 is proposed. It is shown that care must be taken in selecting the rank used in the test statistic. The method complements previous work by extending applicability beyond the Gaussian case, while considering tests of zero effect rather than testing the parametric hypothesis given by the null space of the component’s smoothing penalty. The proposed p-values are routine and efficient to compute from a fitted model, without requiring extra model fits or null distribution simulation.
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
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Wood, S. N. (2011) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2010.00749.x · Cited by 7,537
Self-citationHastie, T., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013604 · Cited by 3,409
Computing the distribution of quadratic forms in normal variables
IMHOF, J. P. (1961) · Biometrika · doi:10.1093/biomet/48.3-4.419 · Cited by 1,217
Matrix Algebra from a Statistician's Perspective
Ziegel, E. R., & Harville, D. A. (1998) · Technometrics · doi:10.2307/1270666 · Cited by 880
Fundamentals of Matrix Computations.
S., G. W., & Watkins, D. S. (1992) · Mathematics of Computation · doi:10.2307/2153000 · Cited by 856
Bayesian “Confidence Intervals” for the Cross-Validated Smoothing Spline
Wahba, G. (1983) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1983.tb01239.x · Cited by 637
Algorithm AS 155: The Distribution of a Linear Combination of χ 2 Random Variables
Davies, R. B. (1980) · Applied Statistics · doi:10.2307/2346911 · Cited by 589
Eine neue Herleitung des Exponentialgesetzes in der Wahrscheinlichkeitsrechnung
Lindeberg, J. W. (1922) · Mathematische Zeitschrift · doi:10.1007/bf01494395 · Cited by 400
Scheipl, F., Greven, S., & Küchenhoff, H. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.10.022 · Cited by 321
Cited by
Bates, D. M., Kliegl, R. M., Vasishth, S., & Baayen, R. H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.04967 · Cited by 1,008
Gennatas, E. D., Avants, B. B., Wolf, D. H., Satterthwaite, T. D., Ruparel, K., Ciric, R., Hakonarson, H., Gur, R. E., & Gur, R. C. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3550-16.2017 · Cited by 302
Heterogeneous global crop yield response to biochar: a meta-regression analysis
Crane-Droesch, A., Abiven, S., Jeffery, S., & Torn, M. S. (2013) · Environmental Research Letters · doi:10.1088/1748-9326/8/4/044049 · Cited by 297
A canonical trajectory of executive function maturation from adolescence to adulthood
Tervo-Clemmens, B., Calabro, F. J., Parr, A. C., Fedor, J., Foran, W., & Luna, B. (2023) · Nature Communications · doi:10.1038/s41467-023-42540-8 · Cited by 241
Spaite, D. W., Hu, C., Bobrow, B. J., Chikani, V., Barnhart, B., Gaither, J. B., Denninghoff, K. R., Adelson, P. D., Keim, S. M., Viscusi, C., et al. (2017) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2016.08.007 · Cited by 187
Metadata
- Authors
- Wood, S. N.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-10-19
- Volume
- 100
- Issue
- 1
- Pages
- 221-228
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2144083456
- Cited by
- 388
- References
- 18
- FWCI
- 9.59
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: On p-values for smooth components of an extended generalized additive model. https://app.cochat.ai/papers/10.1093/biomet/ass048
- The paper
- Wood, S. N. (2013). On p-values for smooth components of an extended generalized additive model. Biometrika. https://doi.org/10.1093/biomet/ass048
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.
