Spline Models for Observational Data

Hyperparameter estimation for uncertainty quantification in mesoscale carbon dioxide inversions

Wu, L., Bocquet, M., Chevallier, F., Lauvaux, T., & Davis, K. J. (2013) · Tellus B · doi:10.3402/tellusb.v65i0.20894 · Cited by 39

Incompressible Deformation Estimation Algorithm (IDEA) From Tagged MR Images

Liu, X., Abd‐Elmoniem, K. Z., Stone, M., et al. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2168825 · Cited by 38

Dynamic system identification with order statistics

Greblicki, W., & Pawlak, M. (1994) · IEEE Transactions on Information Theory · doi:10.1109/18.333862 · Cited by 38

Regularization of Case-Specific Parameters for Robustness and Efficiency

Lee, Y., MacEachern, S. N., & Jung, Y. (2012) · Statistical Science · doi:10.1214/11-sts377 · Cited by 38

Relativistic dynamics of stars near a supermassive black hole

Hamers, A. S., Zwart, S. P., & Merritt, D. R. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu1126 · Cited by 39

Population Encoding With Hodgkin–Huxley Neurons

Lazar, A. A. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2037040 · Cited by 38

Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data

Gnecco, G., & Sanguineti, M. (2009) · Neural Computation · doi:10.1162/neco.2009.05-08-786 · Cited by 38

Boosted multivariate trees for longitudinal data

Pande, A., Li, L., Rajeswaran, J., Ehrlinger, J., Kogalur, U. B., Blackstone, E. H., & Ishwaran, H. (2016) · Machine Learning · doi:10.1007/s10994-016-5597-1 · Cited by 36

High Spatio-Temporal Resolution Deformation Time Series With the Fusion of InSAR and GNSS Data Using Spatio-Temporal Random Effect Model

Liu, N., Dai, W., Santerre, R., et al. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2854736 · Cited by 38

Backstepping for Partially Unknown Nonlinear Systems Using Gaussian Processes

Capone, A., & Hirche, S. (2019) · IEEE Control Systems Letters · doi:10.1109/lcsys.2018.2890467 · Cited by 38

Generalizing Transfer Bayesian Optimization to Source-Target Heterogeneity

Min, A. T. W., Gupta, A., & Ong, Y.-S. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3017644 · Cited by 38

A linked geomorphological and geophysical modelling methodology applied to an active landslide

Boyd, J. P., Chambers, J. E., Wilkinson, P. B., et al. (2021) · Landslides · doi:10.1007/s10346-021-01666-w · Cited by 38

Ridge regression optimization using a harmonious approach

Forrester, J. B., & Kalivas, J. H. (2004) · Journal of Chemometrics · doi:10.1002/cem.883 · Cited by 38

An Assessment of Methods and Remote-Sensing Derived Covariates for Regional Predictions of 1 km Daily Maximum Air Temperature

Parmentier, B., McGill, B. J., Wilson, A. M., Regetz, J., Jetz, W., Guralnick, R., Tuanmu, M., Robinson, N., & Schildhauer, M. P. (2014) · Remote Sensing · doi:10.3390/rs6098639 · Cited by 38

Minimax signal detection in ill-posed inverse problems

Ingster, Y. I., Sapatinas, T., & Suslina, I. A. (2012) · The Annals of Statistics · doi:10.1214/12-aos1011 · Cited by 38

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

Sampling Bias and Logistic Models

McCullagh, P. J. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00660.x · Cited by 38

Multiway Spectral Clustering: A Margin-based Perspective

Zhang, Z., & Jordan, M. I. (2008) · Cited by 38

Spatial Interpolation

Bhattacharjee, S., Ghosh, S. K., & Chen, J. (2019) · Studies in computational intelligence · doi:10.1007/978-981-13-8664-0_2 · Cited by 38

Generalized sampling: a variational approach .II. Applications

Kybic, J., Blu, T., & Unser, M. A. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.800386 · Cited by 38

Reproducing kernels of generalized Sobolev spaces via a Green function approach with distributional operators

Fasshauer, G. E., & Ye, Q. (2011) · Numerische Mathematik · doi:10.1007/s00211-011-0391-2 · Cited by 37

Validation and inter-comparison of three methodologies for interpolating daily precipitation and temperature across Canada

Newlands, N. K., Davidson, A., Howard, A., et al. (2010) · Environmetrics · doi:10.1002/env.1044 · Cited by 37

Interactive Natural Image Segmentation via Spline Regression

Xiang, S., Nie, F., Zhang, C., et al. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2018570 · Cited by 37

Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation

Lee, A. X., Gupta, A., Lu, H. H., Levine, S., & Abbeel, P. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354120 · Cited by 34

Tornado Detection with Support Vector Machines

Trafali̇s, T. B., İnce, H., & Richman, M. B. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44864-0_30 · Cited by 37

Distributed Kernel Regression: An Algorithm for Training Collaboratively

Predd, J. B., Kulkarni, S. R., & Poor, H. V. (2006) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw.2006.1633840 · Cited by 37

Primal and dual model representations in kernel-based learning

Suykens, J. A. K., Alzate, C., & Kristiaan Pelckmans, A. (2010) · Statistics Surveys · doi:10.1214/09-ss052 · Cited by 41

Scale measures for bandwidth selection

Janssen, P., Marron, J. S., Veraverbeke, N., et al. (1995) · Journal of nonparametric statistics · doi:10.1080/10485259508832654 · Cited by 37

Functional Robust Support Vector Machines for Sparse and Irregular Longitudinal Data

Wu, Y., & Liu, Y. (2013) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2012.680823 · Cited by 37

Radial basis function approximations as smoothing splines

Hickernell, F. J., & Hon, Y. C. (1999) · Applied Mathematics and Computation · doi:10.1016/s0096-3003(98)10012-7 · Cited by 37

Kernel-based methods for inversion of the Radon transform on SO(3) and their applications to texture analysis

van den Boogaart, K. G., Hielscher, R., Prestin, J., et al. (2006) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2005.12.003 · Cited by 37

Automated smoothing of image and other regularly spaced data

Berman, M. (1994) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.291451 · Cited by 37

Estimation and model selection in constrained deconvolution

Verotta, D. (1993) · Annals of Biomedical Engineering · doi:10.1007/bf02368641 · Cited by 37

Analysis of computationally demanding models with continuous and categorical inputs

Storlie, C. B., Reich, B. J., Helton, J. C., Swiler, L. P., & Sallaberry, C. J.-M. (2012) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.11.018 · Cited by 36

Computing Reproducing Kernels with Arbitrary Boundary Constraints

Dalzell, C. J., & Ramsay, J. O. (1993) · SIAM Journal on Scientific Computing · doi:10.1137/0914032 · Cited by 36

Fitting forestry models using generalized additive models: a taper model example

Robinson, A. P., Lane, S. E., & Thérien, G. (2011) · Canadian Journal of Forest Research · doi:10.1139/x11-095 · Cited by 36

Risk of POP mixtures on the Arctic food chain

Villa, S., Migliorati, S., Monti, G. S., et al. (2017) · Environmental Toxicology and Chemistry · doi:10.1002/etc.3671 · Cited by 36

Assessing local and regional economic impacts of climatic extremes and feasibility of adaptation measures in Dutch arable farming systems

Diogo, V., Reidsma, P., Schaap, B. F., Andrée, B. P. J., & Koomen, E. (2017) · Agricultural Systems · doi:10.1016/j.agsy.2017.06.013 · Cited by 36

Bayesian joint muographic and gravimetric inversion applied to volcanoes

Barnoud, A., Cayol, V., Niess, V., et al. (2019) · Geophysical Journal International · doi:10.1093/gji/ggz300 · Cited by 36

Distributed Online Learning With Multiple Kernels

Hong, S., & Chae, J. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3105146 · Cited by 36

Data Mining Methods and Applications

Tsui, K., Chen, V., Jiang, W., et al. (2006) · Springer eBooks · doi:10.1007/978-1-84628-288-1_36 · Cited by 36

Clustering of functional data in a low-dimensional subspace

Yamamoto, M. (2012) · Advances in Data Analysis and Classification · doi:10.1007/s11634-012-0113-3 · Cited by 36

Bayesian Adaptive Smoothing Splines Using Stochastic Differential Equations

Yu, Y., Simpson, D., Lindgren, F., & Rue, H. (2014) · Bayesian Analysis · doi:10.1214/13-ba866 · Cited by 40

Clustering threshold gradient descent regularization: with applications to microarray studies

Ma, S., & Huang, J. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl632 · Cited by 36

Smooth inversion for ground surface temperature histories: estimating the optimum regularization parameter by generalized cross-validation

Rath, V., & Mottaghy, D. (2007) · Geophysical Journal International · doi:10.1111/j.1365-246x.2007.03587.x · Cited by 36

Joint Surface Modeling With Thin-Plate Splines

Boyd, S. K., Ronsky, J. L., Lichti, D. D., et al. (1999) · Journal of Biomechanical Engineering · doi:10.1115/1.2835083 · Cited by 36

Selection criteria for scatterplot smoothers

Efron, B. (2001) · The Annals of Statistics · doi:10.1214/aos/1009210549 · Cited by 36

Kernel Basis Pursuit

Guigue, V., Rakotomamonjy, A., & Canu, S. (2005) · Lecture notes in computer science · doi:10.1007/11564096_18 · Cited by 36

Constructing and reconstructing characters, words, and sentences by synthesizing writing motions

Fujioka, H., Kano, H., NAKATA, H., et al. (2006) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2005.851344 · Cited by 36

Two constrained deconvolution methods using spline functions

Verotta, D. (1993) · Journal of Pharmacokinetics and Biopharmaceutics · doi:10.1007/bf01059117 · Cited by 36

Optimum nonlinear filtering

Haykin, S., Yee, P., & Derbez, E. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650104 · Cited by 36

Spatial Regression Models, Response Surfaces, and Process Optimization

O'Connell, M. A., & Wolfinger, R. D. (1997) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1997.10474738 · Cited by 36

Unifying scene registration and trajectory optimization for learning from demonstrations with application to manipulation of deformable objects

Lee, A. X., Huang, S. H., Hadfield-Menell, D., Tzeng, E., & Abbeel, P. (2014) · 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2014.6943185 · Cited by 32

Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation

Alvarez, O., Guo, Q., Klinger, R. C., Li, W., & Doherty, P. (2013) · International Journal of Climatology · doi:10.1002/joc.3835 · Cited by 35

An iterated graph laplacian approach for ranking on manifolds

Zhou, X., Belkin, M., & Srebro, N. (2011) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020556 · Cited by 35

Spline analysis of Holocene sediment magnetic records: Uncertainty estimates for field modeling

Panovska, S., Finlay, C. C., Donadini, F., & Hirt, A. M. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2011jb008813 · Cited by 36

Randomly shifted lattice rules for unbounded integrands

Kuo, F. Y., Wasilkowski, G. W., & Waterhouse, B. J. (2006) · Journal of Complexity · doi:10.1016/j.jco.2006.04.006 · Cited by 35

Greenland accumulation: An error model

Cogley, J. G. (2004) · Journal of Geophysical Research Atmospheres · doi:10.1029/2003jd004449 · Cited by 35

A spectral series approach to high-dimensional nonparametric regression

Lee, A. B., & Izbicki, R. (2016) · Electronic Journal of Statistics · doi:10.1214/16-ejs1112 · Cited by 35

Direct Reconstruction of Two-Dimensional Currents in Thin Films from Magnetic-Field Measurements

Meltzer, A. Y., Levin, E., & Zeldov, E. (2017) · Physical Review Applied · doi:10.1103/physrevapplied.8.064030 · Cited by 35

More efficient approximation of smoothing splines via space-filling basis selection

Meng, C., Zhang, X., Zhang, J., Zhong, W., & Ma, P. (2020) · Biometrika · doi:10.1093/biomet/asaa019 · Cited by 34

LiDARTag: A Real-Time Fiducial Tag System for Point Clouds

Huang, J.-K., Wang, S., Ghaffari, M., & Grizzle, J. W. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3070302 · Cited by 45

Flexible hazard ratio curves for continuous predictors in multi-state models

Cadarso-Suárez, C., Meira‐Machado, L., Kneib, T., & Gudé, F. (2010) · Statistical Modelling · doi:10.1177/1471082x0801000303 · Cited by 35

Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results

Gaggero, M., Gnecco, G., & Sanguineti, M. (2012) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-012-0118-2 · Cited by 35

Least Square Regularized Regression in Sum Space

Xu, Y., Chen, D., Li, H., et al. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2242091 · Cited by 35

Spatial interpolation of the parameters of a rainfall model from ground-based data

de Guenni, L. B., & Hutchinson, M. F. (1998) · Journal of Hydrology · doi:10.1016/s0022-1694(98)00215-7 · Cited by 35

Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods

Rad, K. R., & Paninski, L. (2010) · Network Computation in Neural Systems · doi:10.3109/0954898x.2010.532288 · Cited by 35

Reduction of Fuel Consumption on Hybrid Marine Power Plants by Strategic Loading With Energy Storage Devices

Miyazaki, M. R., Sorensen, A. J., & Vartdal, B. J. (2016) · IEEE Power and Energy Technology Systems Journal · doi:10.1109/jpets.2016.2621117 · Cited by 35

Predicted Residual Error Sum of Squares of Mixed Models: An Application for Genomic Prediction

Xu, S. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.038059 · Cited by 35

A note on the choice and the estimation of Kriging models for the analysis of deterministic computer experiments

Ginsbourger, D., Dupuy, D., Badea, A., et al. (2009) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.741 · Cited by 35

Approximation of a Thin Plate Spline Smoother Using Continuous Piecewise Polynomial Functions

Roberts, S., Hegland, M., & Altas, I. (2003) · SIAM Journal on Numerical Analysis · doi:10.1137/s0036142901383296 · Cited by 34

Spline-based non-parametric regression for periodic functions and its application to directional tuning of neurons

Kaufman, C. G., Ventura, V., & Kass, R. E. (2005) · Statistics in Medicine · doi:10.1002/sim.2104 · Cited by 34

Uncertainty quantification in autoencoders predictions: Applications in aerodynamics

Saetta, E., Tognaccini, R., & Iaccarino, G. (2024) · Journal of Computational Physics · doi:10.1016/j.jcp.2024.112951 · Cited by 35

D-MORPH regression for modeling with fewer unknown parameters than observation data

Li, G., Rey-de-Castro, R., & RABITZ, H. A. (2012) · Journal of Mathematical Chemistry · doi:10.1007/s10910-012-0004-z · Cited by 34

A new sparse variable selection via random-effect model

Lee, Y., & Oh, H. (2013) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2013.11.016 · Cited by 34

A method for regional climate change detection using smooth temporal patterns

Ribes, A., Azäis, J., & Planton, S. (2009) · Climate Dynamics · doi:10.1007/s00382-009-0670-0 · Cited by 34

Portal‐systemic shunting in patients with fibrosis or cirrhosis due to chronic hepatitis C: the minimal model for measuring cholate clearances and shunt

Everson, G. T., MARTUCCI, M. A., Shiffman, M. L., et al. (2007) · Alimentary Pharmacology & Therapeutics · doi:10.1111/j.1365-2036.2007.03389.x · Cited by 34

Optimal A Priori Discretization Error Bounds for Geodesic Finite Elements

Grohs, P., Hardering, H., & Sander, O. (2014) · Foundations of Computational Mathematics · doi:10.1007/s10208-014-9230-z · Cited by 34

Derivative-Free Online Learning of Inverse Dynamics Models

Romeres, D., Zorzi, M., Camoriano, R., Traversaro, S., & Chiuso, A. (2019) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2019.2891222 · Cited by 33

Continuous-Time DC Kernel—A Stable Generalized First-Order Spline Kernel

Chen, T. (2018) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2018.2825365 · Cited by 33

Convolution sampling and reconstruction of signals in a reproducing kernel subspace

Nashed, M. Z., Sun, Q., & Xian, J. (2012) · Proceedings of the American Mathematical Society · doi:10.1090/s0002-9939-2012-11644-2 · Cited by 34

Is There a Curse of Dimensionality for Contraction Fixed Points in the Worst Case?

Rust, J. P., Traub, J. F., & Wozniakowski, H. (2002) · Econometrica · doi:10.1111/1468-0262.00276 · Cited by 34

From curve design algorithms to the design of rigid body motions

Höfer, M., Pottmann, H., & Ravani, B. (2004) · The Visual Computer · doi:10.1007/s00371-003-0221-3 · Cited by 34

A data-adaptive knot selection scheme for fitting splines

He, X., Shen, L., & Shen, Z. (2001) · IEEE Signal Processing Letters · doi:10.1109/97.917695 · Cited by 34

Information Geometry for Landmark Shape Analysis: Unifying Shape Representation and Deformation

Peter, A. M., & Rangarajan, A. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.69 · Cited by 34

Fault Diagnosis of Time-Varying Parameter Systems With Application in MEMS LCRs

Izadian, A., Khayyer, P., & Famouri, P. F. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2008.2010095 · Cited by 34

A comparative study of subgroup identification methods for differential treatment effect: Performance metrics and recommendations

Alemayehu, D., Chen, Y., & Markatou, M. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217710570 · Cited by 33

Single-trial estimation of multichannel evoked-potential measurements

Ranta-aho, P. O., Koistinen, A. S., Ollikainen, J. O., et al. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2002.807654 · Cited by 34

Finding large‐scale spatial trends in massive, global, environmental datasets

Johannesson, G., & Cressie, N. (2003) · Environmetrics · doi:10.1002/env.624 · Cited by 34

A stable finite difference ansatz for higher order differentiation of non-exact data

Anderssen, B., de Hoog, F., & Hegland, M. (1998) · Bulletin of the Australian Mathematical Society · doi:10.1017/s0004972700032196 · Cited by 34

Interpolation splines minimizing a semi-norm

Hayotov, A. R., Milovanović, G. V., & Shadimetov, K. (2013) · CALCOLO · doi:10.1007/s10092-013-0080-x · Cited by 33

Selecting the most appropriate time points to profile in high-throughput studies

Kleyman, M., Sefer, E., Nicola, T., Espinoza, C. R., Chhabra, D., Hagood, J. S., Kaminski, N., Ambalavanan, N., & Bar‐Joseph, Z. (2017) · eLife · doi:10.7554/elife.18541 · Cited by 33

Online Learning Algorithms Can Converge Comparably Fast as Batch Learning

Lin, J., & Zhou, D. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2677970 · Cited by 33

Sparse Modal Additive Model

Chen, H., Wang, Y., Zheng, F., et al. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3005144 · Cited by 33

The Induced Mw 5.0 March 2020 West Texas Seismic Sequence

Skoumal, R. J., Kaven, J. O., Barbour, A. J., et al. (2020) · Journal of Geophysical Research Solid Earth · doi:10.1029/2020jb020693 · Cited by 33

Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Yang, Y., Dong, Z., Meng, Y., et al. (2021) · Machines · doi:10.3390/machines9010013 · Cited by 33

Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects

Dewulf, P., Stock, M., & Bernard De Baets, C. (2021) · Pharmaceuticals · doi:10.3390/ph14050429 · Cited by 34

An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

Li, F., Zhu, H., Luo, Z., et al. (2021) · Remote Sensing · doi:10.3390/rs13152938 · Cited by 33

Learning for mismatch removal via graph attention networks

Jiang, X., Wang, Y., Fan, A., & Ma, J. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.06.009 · Cited by 33

Earth’s electromagnetic environment

Chave, A. D., & Jones, A. G. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139020138.004 · Cited by 33

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Observational Studies

HESS, D. R. (2023) · Respiratory Care · doi:10.4187/respcare.11170 · Cited by 65

The Effects of Video Observational Training on Video and Live Observational Proficiency

Eckrich, J., Widule, C. J., Shrader, R. A., et al. (1994) · Journal of Teaching in Physical Education · doi:10.1123/jtpe.13.3.216 · Cited by 12

The value of including observational studies in systematic reviews was unclear: a descriptive study

Seida, J. C., Dryden, D. M., & Hartling, L. (2014) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.06.014 · Cited by 4