The DOI matches its Crossref record.
Spline Models for Observational Data
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“As shown in [18], Equation (1) can be analytically solved efficiently; details are provided in Appendix 9.1.”
From Learning from Demonstrations Through the Use of Non-rigid Registration (2016) · cites this paper as [18]
Citations
Cited by
Sorted by citations
Items 801–900 of 1,001
Varying-coefficient functional linear regression
Wu, Y., Fan, J., & Müller, H.-G. (2010) · Bernoulli · doi:10.3150/09-bej231 · Cited by 45
Vandenberg‐Rodes, A., Moftakhari, H. R., AghaKouchak, A., Shahbaba, B., Sanders, B. F., & Matthew, R. A. (2016) · Journal of Geophysical Research Oceans · doi:10.1002/2016jc012084 · Cited by 47
Generalized survival models for correlated time‐to‐event data
Liu, X., Yudi Pawitan, Y., & Clements, M. (2017) · Statistics in Medicine · doi:10.1002/sim.7451 · Cited by 45
Understanding the Stochastic Partial Differential Equation Approach to Smoothing
Miller, D. L., Glennie, R., & Seaton, A. E. (2020) · Journal of Agricultural, Biological and Environmental Statistics · doi:10.1007/s13253-019-00377-z · Cited by 46
Comparison of Kriging and Neural Networks With Application to the Exploitation of a Slate Mine
Matías, J. M., Vaamonde, A., Taboada, J., et al. (2004) · Mathematical Geology · doi:10.1023/b:matg.0000029300.66381.dd · Cited by 45
Elster, C., Schubert, F., Link, A., Walzel, M., Seifert, F. C., & Rinneberg, H. H. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20500 · Cited by 45
Period Analysis of Variable Stars by Robust Smoothing
Oh, H., Nychka, D. W., Brown, T., & Charbonneau, P. (2004) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2004.00423.x · Cited by 44
Inverse Problems and Structured-Population Dynamics
Wood, S. N. (1997) · Springer eBooks · doi:10.1007/978-1-4615-5973-3_19 · Cited by 44
Linear unlearning for cross-validation
Hansen, L. K., & Larsen, J. O. (1996) · Advances in Computational Mathematics · doi:10.1007/bf02124747 · Cited by 45
Constructing synthetics from deep earth tomographic models
Ni, S., Ding, X., & Helmberger, D. V. (2000) · Geophysical Journal International · doi:10.1046/j.1365-246x.2000.00982.x · Cited by 45
A Bayesian Approach to Nonparametric Bivariate Regression
Smith, M. S., & Kohn, R. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10473674 · Cited by 45
Time series analysis and prediction on complex dynamical behavior observed in a blast furnace
Miyano, T., Kimoto, S., Shibuta, H., et al. (2000) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(99)00135-9 · Cited by 44
Seismic inversion based on proximal objective function optimization algorithm
Dai, R., Zhang, F., & Liu, H. (2016) · Geophysics · doi:10.1190/geo2014-0590.1 · Cited by 44
Rizzo, R., Medeiros, L. G., de Mello, D. C., et al. (2019) · Geoderma · doi:10.1016/j.geoderma.2019.114018 · Cited by 44
A Function Representation for Learning in Banach Spaces
Micchelli, C. A., & Pontil, M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27819-1_18 · Cited by 44
Periodic boxcar deconvolution and diophantine approximation
Johnstone, I. M., & Raimondo, M. (2004) · The Annals of Statistics · doi:10.1214/009053604000000391 · Cited by 44
Forest climatology: estimation and use of daily climatological data for Bavaria, Germany
Xia, Y., Fabian, P., Winterhalter, M., et al. (2001) · Agricultural and Forest Meteorology · doi:10.1016/s0168-1923(00)00210-0 · Cited by 44
Identification of Structured Nonlinear Systems
Hsu, K., Poolla, K. R., & Vincent, T. L. (2008) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.2006928 · Cited by 44
A space–time varying coefficient model: The equity of service accessibility
Serban, N. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas473 · Cited by 43
Diez, M., He, W., Campana, E. F., et al. (2013) · Journal of Marine Science and Technology · doi:10.1007/s00773-013-0235-0 · Cited by 43
Semiparametric Estimation of Additive Quantile Regression Models by Two-Fold Penalty
Lian, H. (2012) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2012.693851 · Cited by 43
Gutman, R., & Rubin, D. B. (2012) · Statistics in Medicine · doi:10.1002/sim.5627 · Cited by 43
Regularizing Multilayer Perceptron for Robustness
Dey, P., Nag, K., Pal, T., & Pal, N. R. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2664143 · Cited by 43
Background error covariance functions for Doppler radial‐wind analysis
Xu, Q., & Gong, J. (2003) · Quarterly Journal of the Royal Meteorological Society · doi:10.1256/qj.02.129 · Cited by 43
Hierarchical spatial models for predicting tree species assemblages across large domains
Finley, A. O., Banerjee, S., & McRoberts, R. E. (2009) · The Annals of Applied Statistics · doi:10.1214/09-aoas250 · Cited by 43
Group-Wise Point-Set Registration Using a Novel CDF-Based Havrda-Charvát Divergence
Chen, T., Vemuri, B. C., Rangarajan, A., & Eisenschenk, S. J. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0261-x · Cited by 42
Path consistent model selection in additive risk model via Lasso
Leng, C.-L., & Ma, S.-G. (2007) · Statistics in Medicine · doi:10.1002/sim.2834 · Cited by 49
Embedding new data points for manifold learning via coordinate propagation
Xiang, S., Nie, F., Song, Y., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-008-0161-3 · Cited by 43
Classical Geostatistical Methods
Zimmerman, D. L., & Stein, M. L. (2010) · Chapman & Hall/CRC interdisciplinary statistics series/Interdisciplinary statistics · doi:10.1201/9781420072884-c3 · Cited by 43
Prestack structurally constrained impedance inversion
Hamid, H., Pidlisecky, A., & Lines, L. R. (2017) · Geophysics · doi:10.1190/geo2016-0703.1 · Cited by 42
Propensity score analysis methods with balancing constraints: A Monte Carlo study
Li, Y., & Li, L. (2021) · Statistical Methods in Medical Research · doi:10.1177/0962280220983512 · Cited by 42
Variational Correction of Aircraft Temperature Bias in the NCEP’s GSI Analysis System
Zhu, Y., Derber, J., Purser, R. J., et al. (2015) · Monthly Weather Review · doi:10.1175/mwr-d-14-00235.1 · Cited by 42
Model diagnostics for smoothing spline ANOVA models
Gu, C. (2004) · Canadian Journal of Statistics · doi:10.2307/3316020 · Cited by 42
From Convex to Nonconvex: A Loss Function Analysis for Binary Classification
Zhao, L., Mammadov, M. A., & Yearwood, J. L. (2010) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2010.57 · Cited by 42
Bayesian Approximate Kernel Regression With Variable Selection
Crawford, L. A., Wood, K. C., Zhou, X., & Mukherjee, S. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1361830 · Cited by 41
Rapid automated landmarking for morphometric analysis of three‐dimensional facial scans
Li, M., Cole, J. B., Manyama, M. F., et al. (2017) · Journal of Anatomy · doi:10.1111/joa.12576 · Cited by 42
A comparison of linear discriminant analysis and ridge classifier on Twitter data
Singh, A., Prakash, B. S., & Chandrasekaran, K. (2016) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2016.7813704 · Cited by 45
Adding bias to reduce variance in psychological results: A tutorial on penalized regression
Helwig, N. E. (2017) · The Quantitative Methods for Psychology · doi:10.20982/tqmp.13.1.p001 · Cited by 41
Point Set Registration with Global-Local Correspondence and Transformation Estimation
Zhang, S., Yang, Y., Yang, K., et al. (2017) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.291 · Cited by 42
Gupta, H., Fageot, J., & Unser, M. A. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2860549 · Cited by 42
Zhang, M., de Leon, C., & Migliaccio, K. W. (2018) · Hydrological Sciences Journal · doi:10.1080/02626667.2018.1444767 · Cited by 44
Self-Similarity: Part II—Optimal Estimation of Fractal Processes
Blu, T., & Unser, M. A. (2007) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.890845 · Cited by 42
A generalised linear model for catch allocation: an example from Australia's Northern Prawn Fishery
Venables, W. N., & Dichmont, C. M. (2004) · Fisheries Research · doi:10.1016/j.fishres.2004.08.017 · Cited by 42
Additive risk survival model with microarray data
Ma, S., & Huang, J. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-192 · Cited by 41
A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion
Wills, J., Agarwal, S., & Belongie, S. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-6660-3 · Cited by 40
Free-Knot Polynomial Splines with Confidence Intervals
Mao, W., & Zhao, L. H. (2003) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1046/j.1369-7412.2003.00422.x · Cited by 42
Efficient computation of smoothing splines via adaptive basis sampling
Ma, P., Huang, J. Z., & Zhang, N. (2015) · Biometrika · doi:10.1093/biomet/asv009 · Cited by 38
Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming
Ni, J., & Rockett, P. I. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2306994 · Cited by 41
Optimal dose‐finding designs with correlated continuous and discrete responses
Fedorov, V. V., Wu, Y., & Zhang, R. (2011) · Statistics in Medicine · doi:10.1002/sim.4388 · Cited by 41
EFFECTIVENESS OF SUPPORT VECTOR MACHINE FOR CRIME HOT-SPOTS PREDICTION
Kianmehr, K., & Alhajj, R. (2008) · Applied Artificial Intelligence · doi:10.1080/08839510802028405 · Cited by 41
Individualized performance prediction of sleep-deprived individuals with the two-process model.
Rajaraman, S., Gribok, A., Wesensten, N., Balkin, T., & Reifman, J. (2008) · Journal of applied physiology · doi:10.1152/japplphysiol.00877.2007 · Cited by 45
Iglesias, J., González–Warleta, M., Castro-Hermida, J. A., et al. (2016) · Parasites & Vectors · doi:10.1186/s13071-016-1892-8 · Cited by 41
Isotropic covariance functions on graphs and their edges
Anderes, E., Møller, J., & Rasmussen, J. G. (2020) · The Annals of Statistics · doi:10.1214/19-aos1896 · Cited by 41
State estimation for deformable objects by point registration and dynamic simulation
Tang, T., Fan, Y., Lin, H.-C., & Tomizuka, M. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206058 · Cited by 41
Online Graph-Adaptive Learning With Scalability and Privacy
Shen, Y., Leus, G. J. T., & Giannakis, G. B. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2904922 · Cited by 41
Asymptotic theory of penalized splines
Xiao, L. (2019) · Electronic Journal of Statistics · doi:10.1214/19-ejs1541 · Cited by 41
Nonparametric Regression via Variance-Adjusted Gradient Boosting Gaussian Process Regression
Lu, H., Chen, J.-S., & Liao, W.-C. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2953728 · Cited by 42
Variances for Maximum Penalized Likelihood Estimates Obtained Via the Em Algorithm
Segal, M. R., Bacchetti, P., & Jewell, N. P. (1994) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1994.tb01983.x · Cited by 41
Spline smoothing with model-based penalties
Ramsay, J. O., Heckman, N., & Silverman, B. W. (1997) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03200573 · Cited by 41
Kononov, J., Durso, C. S., Reeves, D., et al. (2012) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2280-01 · Cited by 41
FORECASTING VOLATILITY WITH THE MULTIFRACTAL RANDOM WALK MODEL
Duchon, J., Robert, R., & Vargas, V. (2010) · Mathematical Finance · doi:10.1111/j.1467-9965.2010.00458.x · Cited by 41
Data Visualization and Dimensionality Reduction Using Kernel Maps With a Reference Point
Suykens, J. A. K. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2000807 · Cited by 41
On nonparametric regression for IID observations in a general setting
Efromovich, S. (1996) · The Annals of Statistics · doi:10.1214/aos/1032526960 · Cited by 41
Multi-criteria decision making - an approach to setting priorities in health care
Nobre, F. F., Trotta, L. T. F., & Gomes, L. F. A. M. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19991215)18:23 · Cited by 104
A simple approach for varying-coefficient model selection
Leng, C. (2008) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2008.10.009 · Cited by 40
Nonparametric Tikhonov Regularized NMF and Its Application in Cancer Clustering
Mirzal, A. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2328342 · Cited by 40
Flaxman, S., Wang, Y.-X., & Smola, A. J. (2015) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783300 · Cited by 40
Modeling maximum daily temperature using a varying coefficient regression model
Li, H., Deng, X., Kim, D., et al. (2014) · Water Resources Research · doi:10.1002/2013wr014243 · Cited by 40
Zhang, T., Wu, J., Li, F., Caffo, B. S., & Boatman‐Reich, D. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.988213 · Cited by 37
Modeling outcomes of soccer matches
Tsokos, A., Narayanan, S., Kosmidis, I., et al. (2018) · Machine Learning · doi:10.1007/s10994-018-5741-1 · Cited by 40
Bayesian Projected Calibration of Computer Models
Xie, F., & Xu, Y. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1753519 · Cited by 40
Functional Regression Control Chart
Centofanti, F., Lepore, A., Menafoglio, A., et al. (2020) · Technometrics · doi:10.1080/00401706.2020.1753581 · Cited by 40
Geodesic Shooting and Diffeomorphic Matching Via Textured Meshes
Allassonnière, S., Trouvé, A., & Laurent Younes, B. (2005) · Lecture notes in computer science · doi:10.1007/11585978_24 · Cited by 39
The Partial Linear Model in High Dimensions
Müller, P., & van de Geer, S. (2014) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12124 · Cited by 40
Influence of Lateral Connections on the Structure of Cortical Maps
Carreira-Perpiñán, M. Á., & Goodhill, G. J. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00281.2004 · Cited by 40
Estimating parameters and stochastic functions of one variable using nonlinear measurement models
Bell, B. M., & Pillonetto, G. (2004) · Inverse Problems · doi:10.1088/0266-5611/20/3/001 · Cited by 40
Maximizing the Predictivity of Smooth Deformable Image Warps through Cross-Validation
Bartoli, A. (2008) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-007-0062-1 · Cited by 40
MacCarthy, J., Borchers, B., & Aster, R. C. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2011jb008234 · Cited by 40
Area-to-point Kriging with inequality-type data
Yoo, E., & Kyriakidis, P. C. (2006) · Journal of Geographical Systems · doi:10.1007/s10109-006-0036-7 · Cited by 40
MODEL SELECTION FOR PENALIZED SPLINE SMOOTHING USING AKAIKE INFORMATION CRITERIA
Wager, C. G., Vaida, F., & Kauermann, G. (2007) · Australian & New Zealand Journal of Statistics · doi:10.1111/j.1467-842x.2007.00473.x · Cited by 37
Interpolating point spread function anisotropy
Gentile, M., Courbin, F., & Meylan, G. (2012) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/201219739/pdf · Cited by 41
Tomographic reconstruction using information-weighted spline smoothing
Fessler, J. A. (2005) · Springer eBooks · doi:10.1007/bfb0013800 · Cited by 40
Huang, S. H., Pan, J., Mulcaire, G., & Abbeel, P. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353475 · Cited by 39
An assessment of statistical interpolation methods suited for gridded rainfall datasets
Liu, Y., Zhuo, L., Pregnolato, M., & Han, D. (2021) · International Journal of Climatology · doi:10.1002/joc.7389 · Cited by 41
Data Mining Methods and Applications
Tsui, K., Chen, V., Jiang, W., Yang, F., & Kan, C. (2023) · Springer handbooks · doi:10.1007/978-1-4471-7503-2_38 · Cited by 41
Multiscale Representation and Analysis of Spherical Data by Spherical Wavelets
Li, T. (1999) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827598341463 · Cited by 39
Hallema, D. W., Lafond, J. A., Périard, Y., et al. (2015) · Vadose Zone Journal · doi:10.2136/vzj2014.10.0147 · Cited by 39
On the Detection and Quantification of RFI in L1a Signals Provided by SMOS
Anterrieu, É. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2136350 · Cited by 39
Online semi-parametric learning for inverse dynamics modeling
Romeres, D., Zorzi, M., Camoriano, R., & Chiuso, A. (2016) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2016.7798708 · Cited by 40
Lestari, B., Fatmawati, F., Budiantara, I. N., & Chamidah, N. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1097/1/012091 · Cited by 40
Zhao, H., Huang, W., Xie, T., Wu, X. L., Xie, Y., Feng, S., & Chen, F. (2019) · Theoretical and Applied Climatology · doi:10.1007/s00704-019-02830-y · Cited by 39
Property Rights, Land Disputes and Water Scarcity: Empirical Evidence from Ethiopia
Falco, S. D., Laurent‐Lucchetti, J., Veronesi, M., & Köhlin, G. (2019) · American Journal of Agricultural Economics · doi:10.1093/ajae/aaz036 · Cited by 39
Voorhoeve, R., de Rozario, R., Aangenent, W., & Oomen, T. (2020) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2020.2974140 · Cited by 38
Varying-Coefficient Functional Linear Regression
Wu, Y., Fan, J., & Müller, H. (2010) · Cited by 39
Estimating repeat spectra and genome length from low-coverage genome skims with RESPECT
Sarmashghi, S., Balaban, M., Rachtman, E., Touri, B., Mirarab, S., & Bafna, V. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009449 · Cited by 38
Discriminative Training of Hidden Markov Models for Multiple Pitch Tracking
Bach, F., & Jordan, M. I. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1416347 · Cited by 39
Nonparametric regression, confidence regions and regularization
Davies, P. L., Kovac, A., & Meise, M. (2009) · The Annals of Statistics · doi:10.1214/07-aos575 · Cited by 39
Paulson, J. N., Talukder, H., & Bravo, H. C. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/099457 · Cited by 39
Chen, G., Taylor, P. A., Reynolds, R. C., Leibenluft, E., Pine, D. S., Brotman, M. A., Pagliaccio, D., & Haller, S. P. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120224 · Cited by 42
Spline-based spectrum cartography for cognitive radios
Mateos, G., Bazerque, J. A., & Giannakis, G. B. (2009) · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/acssc.2009.5470044 · Cited by 38
Metadata
- Authors
- Wahba, G.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Wahba, Grace
- Venue
- Society for Industrial and Applied Mathematics eBooks
- Published
- 1990-01-01
- OpenAlex
- W4298876635
- Cited by
- 4,184
- References
- 0
- Citations by year
- 2026: 89 · 2025: 147 · 2024: 135 · 2023: 141 · 2022: 156 · 2021: 191 · 2020: 184 · 2019: 154 · 2018: 165 · 2017: 156 · 2016: 140 · 2015: 152 · 2014: 156 · 2013: 161 · 2012: 163
- FWCI
- 17.89
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Society for Industrial and Applied Mathematics eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- spline models, smoothing splines, nonparametric regression, penalized splines, bandwidth selection, loess
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