Spline Models for Observational Data

A tutorial on support vector regression

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The Elements of Statistical Learning: Data Mining, Inference, and Prediction

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De-noising by soft-thresholding

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Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models

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Shape matching and object recognition using shape contexts

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An overview of statistical learning theory

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Nonrigid registration using free-form deformations: application to breast mr images

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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

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Analysis of Discrete Ill-Posed Problems by Means of the L-Curve

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Flexible smoothing with B-splines and penalties

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Networks for approximation and learning

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Generalized Additive Models

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The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems

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Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning

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Universal approximation bounds for superpositions of a sigmoidal function

Barron, A. R. (1993) · IEEE Transactions on Information Theory · doi:10.1109/18.256500 · Cited by 2,861 · see citing works

Point Set Registration: Coherent Point Drift

Myronenko, A., & Song, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.46 · Cited by 2,774 · see citing works

Construction of correlation functions in two and three dimensions

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Varying-Coefficient Models

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Reconstruction and representation of 3D objects with radial basis functions

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Inverse problems: A Bayesian perspective

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REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems

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Kernel methods in machine learning

Hofmann, T., Schölkopf, B., & Smola, A. J. (2008) · The Annals of Statistics · doi:10.1214/009053607000000677 · Cited by 1,758 · see citing works

Splines: a perfect fit for signal and image processing

Unser, M. A. (1999) · IEEE Signal Processing Magazine · doi:10.1109/79.799930 · Cited by 1,627 · see citing works

A Generalized Representer Theorem

Schölkopf, B., Herbrich, R., & Smola, A. J. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44581-1_27 · Cited by 1,610 · see citing works

Genome-Wide Regression and Prediction with the BGLR Statistical Package

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On the mathematical foundations of learning

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Machine Learning Force Fields

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GAUSSIAN PROCESSES FOR MACHINE LEARNING

Seeger, M. (2004) · International Journal of Neural Systems · doi:10.1142/s0129065704001899 · Cited by 1,518 · see citing works

Regularized multi--task learning

Evgeniou, T., & Pontil, M. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014067 · Cited by 1,473 · see citing works

Does Adaptation to Climate Change Provide Food Security? A Micro‐Perspective from Ethiopia

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KERNEL METHODS IN MACHINE LEARNING

Hofmann, T., Schölkopf, B., & Smola, A. J. (2008) · Cited by 1,408 · see citing works

Convex multi-task feature learning

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TOPEX/POSEIDON tides estimated using a global inverse model

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Generalized additive models for medical research

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A spectral technique for correspondence problems using pairwise constraints

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A Brief Survey of Bandwidth Selection for Density Estimation

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Regularization Networks and Support Vector Machines

Evgeniou, T., Pontil, M., & Poggio, T. (2000) · Advances in Computational Mathematics · doi:10.1023/a:1018946025316 · Cited by 1,191 · see citing works

Input space versus feature space in kernel-based methods

Schölkopf, B., Mika, S., Burges, C. J. C., Knirsch, P., Müller, K. R., Rätsch, G., & Smola, A. J. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788641 · Cited by 1,159 · see citing works

Deformable templates using large deformation kinematics

Christensen, G. E., Rabbitt, R. D., & Miller, M. I. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.536892 · Cited by 1,146 · see citing works

Overview and Recent Advances in Partial Least Squares

Rosipal, R., & Krämer, N. (2006) · Lecture notes in computer science · doi:10.1007/11752790_2 · Cited by 1,116 · see citing works

Iterative Methods for Total Variation Denoising

Vogel, C. R., & Oman, M. E. (1996) · SIAM Journal on Scientific Computing · doi:10.1137/0917016 · Cited by 1,091 · see citing works

Nonconcave penalized likelihood with a diverging number of parameters

B, J. F. A., & Peng, H. (2004) · The Annals of Statistics · doi:10.1214/009053604000000256 · Cited by 1,002 · see citing works

Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks

Poggio, T., & Girosi, F. (1990) · Science · doi:10.1126/science.247.4945.978 · Cited by 1,024 · see citing works

Gaussian Predictive Process Models for Large Spatial Data Sets

Banerjee, S., Gelfand, A. E., Finley, A. O., & Sang, H. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2008.00663.x · Cited by 1,000 · see citing works

Fixed Rank Kriging for Very Large Spatial Data Sets

Cressie, N., & Johannesson, G. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00633.x · Cited by 999 · see citing works

Robust Point Set Registration Using Gaussian Mixture Models

Jian, B., & Vemuri, B. C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.223 · Cited by 682 · see citing works

Review of surrogate modeling in water resources

Razavi, S., Tolson, B. A., & Burn, D. H. (2012) · Water Resources Research · doi:10.1029/2011wr011527 · Cited by 994 · see citing works

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

Srinivas, N., Krause, A., Kakade, S. M., & Seeger, M. (2012) · IEEE Transactions on Information Theory · doi:10.48550/arxiv.0912.3995 · Cited by 2,100 · see citing works

Some Tools for Functional Data Analysis

Ramsay, J. O., & Dalzell, C. J. (1991) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1991.tb01844.x · Cited by 942 · see citing works

Flexible Discriminant Analysis by Optimal Scoring

Hastie, T., Tibshirani, R. J., & Buja, A. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476866 · Cited by 840 · see citing works

Philosophy and the practice of Bayesian statistics

Gelman, A., & Shalizi, C. R. (2013) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.2011.02037.x · Cited by 825 · see citing works

Unified Notation for Data Assimilation : Operational, Sequential and Variational (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice)

Ide, K., Courtier, P., Ghil, M., & Lorenc, A. C. (1997) · Journal of the Meteorological Society of Japan Ser II · doi:10.2151/jmsj1965.75.1b_181 · Cited by 823 · see citing works

Compound facial expressions of emotion

Du, S., Tao, Y., & Martinez, A. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322355111 · Cited by 806 · see citing works

Semi-Supervised Learning on Riemannian Manifolds

Belkin, M., & Niyogi, P. (2004) · Machine Learning · doi:10.1023/b:mach.0000033120.25363.1e · Cited by 803 · see citing works

Bayesian classification with Gaussian processes

Williams, C. K. I., & Barber, D. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.735807 · Cited by 764 · see citing works

Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage

Chambolle, A., Vore, R. A. D., Lee, N.-Y., & Lucier, B. J. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661182 · Cited by 733 · see citing works

Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments

Currin, C., Mitchell, T. J., Morris, M. D., & Ylvisaker, D. (1991) · Journal of the American Statistical Association · doi:10.1080/01621459.1991.10475138 · Cited by 744 · see citing works

Philosophy and the practice of Bayesian statistics

Gelman, A., & Shalizi, C. R. (2010) · Cited by 709 · see citing works

Statistical behavior and consistency of classification methods based on convex risk minimization

Zhang, T. (2004) · The Annals of Statistics · doi:10.1214/aos/1079120130 · Cited by 711 · see citing works

Fast stable direct fitting and smoothness selection for generalized additive models

Wood, S. N. (2006) · Annual Review of Statistics and Its Application · doi:10.1111/j.1467-9868.2007.00646.x · Cited by 9,266 · see citing works

ThemeRiver: visualizing thematic changes in large document collections

Havre, S., Hetzler, E. G., Whitney, P., & Nowell, L. (2002) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.981848 · Cited by 671 · see citing works

A chronology of interpolation: from ancient astronomy to modern signal and image processing

Meijering, E. H. W. (2002) · Proceedings of the IEEE · doi:10.1109/5.993400 · Cited by 665 · see citing works

Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond

Williams, C. K. I. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9_23 · Cited by 684 · see citing works

Modelling topographic potential for erosion and deposition using GIS

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Solving Ill-Conditioned and Singular Linear Systems: A Tutorial on Regularization

Neumaier, A. (1998) · SIAM Review · doi:10.1137/s0036144597321909 · Cited by 646 · see citing works

Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms

Dornhege, G., Blankertz, B., Curio, G., & Müller, K. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827088 · Cited by 632 · see citing works

Neural Networks and Related Methods for Classification

Ripley, B. D. (1994) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1994.tb01990.x · Cited by 627 · see citing works

The connection between regularization operators and support vector kernels

Smola, A. J., Schölkopf, B., & Müller, K. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00032-x · Cited by 626 · see citing works

Parameter estimation for differential equations: a generalized smoothing approach

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Low‐Rank Scale‐Invariant Tensor Product Smooths for Generalized Additive Mixed Models

Wood, S. N. (2006) · Biometrics · doi:10.1111/j.1541-0420.2006.00574.x · Cited by 613 · see citing works

An overview of robust Bayesian analysis

Berger, J. O., Moreno, E., Pericchi, L. R., Bayarri, M. J., Bernardo, J. M., Cano, J. A., de la Horra, J., Martín, J., Insua, D. R., Betrò, B., et al. (1994) · Test · doi:10.1007/bf02562676 · Cited by 591 · see citing works

Bump hunting in high-dimensional data

Friedman, J. H., & Fisher, N. I. (1999) · Statistics and Computing · doi:10.1023/a:1008894516817 · Cited by 605 · see citing works

A review of spline function procedures in R

Perperoglou, A., Sauerbrei, W., Abrahamowicz, M., & Schmid, M. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0666-3 · Cited by 585 · see citing works

An introduction to multivariate adaptive regression splines

Friedman, J. H., & Roosen, C. B. (1995) · Statistical Methods in Medical Research · doi:10.1177/096228029500400303 · Cited by 590 · see citing works

Mathematics for Machine Learning

Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108679930 · Cited by 583 · see citing works

Component selection and smoothing in multivariate nonparametric regression

Lin, Y., & Zhang, H. H. (2006) · The Annals of Statistics · doi:10.1214/009053606000000722 · Cited by 550 · see citing works

Robust Point Matching via Vector Field Consensus

Ma, J., Zhao, J. Y., Tian, J., Yuille, A., & Zhuowen Tu, A. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2307478 · Cited by 566 · see citing works

A Reassessment of the Groundwater Inverse Problem

McLaughlin, D. B., & Townley, L. R. (1996) · Water Resources Research · doi:10.1029/96wr00160 · Cited by 566 · see citing works

Modelling Palaeoecological Time Series Using Generalised Additive Models

Simpson, G. L. (2018) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2018.00149 · Cited by 567 · see citing works

Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models

Chen, X. (2007) · Handbook of econometrics · doi:10.1016/s1573-4412(07)06076-x · Cited by 524 · see citing works

Adaptive sparseness for supervised learning

Figueiredo, M. A. T. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1227989 · Cited by 536 · see citing works

A surface-based technique for warping three-dimensional images of the brain

Thompson, P. M., & Toga, A. W. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.511745 · Cited by 514 · see citing works

Landmark matching via large deformation diffeomorphisms

Joshi, S. C., & Miller, M. I. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.855431 · Cited by 500 · see citing works

Statistical aspects of neural networks

Ripley, B. D. (1993) · Springer eBooks · doi:10.1007/978-1-4899-3099-6_2 · Cited by 515 · see citing works

Estimating treatment effect heterogeneity in randomized program evaluation

Imai, K., & Ratkovic, M. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas593 · Cited by 487 · see citing works

Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation

Galatsanos, N. P., & Katsaggelos, A. K. (1992) · IEEE Transactions on Image Processing · doi:10.1109/83.148606 · Cited by 493 · see citing works

A New Inferential Test for Path Models Based on Directed Acyclic Graphs

Shipley, B. (2000) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem0702_4 · Cited by 493 · see citing works

On image analysis by moments

Liao, S. X., & Pawlak, M. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.485554 · Cited by 493 · see citing works

Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis

Gray, R. J. (1992) · Journal of the American Statistical Association · doi:10.1080/01621459.1992.10476248 · Cited by 489 · see citing works

A tutorial on ν ‐support vector machines

Chen, P.-H., Lin, C., & Schölkopf, B. (2005) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.537 · Cited by 481 · see citing works

Assimilation of Observations, an Introduction (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice)

Talagrand, O. (1997) · Journal of the Meteorological Society of Japan Ser II · doi:10.2151/jmsj1965.75.1b_191 · Cited by 480 · see citing works

Online Prediction of Time Series Data With Kernels

Richard, C., Bermudez, J. C. M., & Honeiné, P. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.2009895 · Cited by 468 · see citing works

Nonlinear System Identification: A User-Oriented Road Map

Schoukens, J., & Lennart Ljung, A. (2019) · IEEE Control Systems · doi:10.1109/mcs.2019.2938121 · Cited by 32 · see citing works

Generalized correlation function: definition, properties, and application to blind equalization

Santamarı́a, I., Pokharel, P. P., & Príncipe, J. C. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.872524 · Cited by 458 · see citing works

Smoothing by Local Regression: Principles and Methods

Cleveland, W. S., & Loader, C. (1996) · Contributions to statistics · doi:10.1007/978-3-642-48425-4_2 · Cited by 460 · see citing works

Selecting a classification method by cross-validation

Schaffer, C. (1993) · Machine Learning · doi:10.1007/bf00993106 · Cited by 442 · see citing works

Hinging hyperplanes for regression, classification, and function approximation

Breiman, L. (1993) · IEEE Transactions on Information Theory · doi:10.1109/18.256506 · Cited by 445 · see citing works

Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint

Rohlfing, T., Maurer, C., Bluemke, D., & Jacobs, M. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.814791 · Cited by 529 · see citing works

Interpolation by regularized spline with tension: I. Theory and implementation

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A new algorithm for non-rigid point matching

Chui, H., & Rangarajan, A. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2000.854733 · Cited by 443 · see citing works

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 61 · see citing works

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

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

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