Spline Models for Observational Data

Smoothing: Local Regression Techniques

Loader, C. (2011) · Springer eBooks · doi:10.1007/978-3-642-21551-3_20 · Cited by 81

Information Consistency of Nonparametric Gaussian Process Methods

Seeger, M., Kakade, S. M., & Foster, D. P. (2008) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.915707 · Cited by 81

Surface Mosaics of the Bladder Reconstructed From Endoscopic Video for Automated Surveillance

Soper, T. D., Porter, M. P., & Seibel, E. J. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2191783 · Cited by 80

Deformation of the Long Valley Caldera, California: inferences from measurements from 1988 to 2001

Langbein, J. O. (2003) · Journal of Volcanology and Geothermal Research · doi:10.1016/s0377-0273(03)00172-0 · Cited by 80

Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression

Yeh, Y.-R., Huang, S., & Lee, Y. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.232 · Cited by 80

Partially Linear Functional Additive Models for Multivariate Functional Data

Wong, R. K. W., Li, Y., & Zhu, Z. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1411268 · Cited by 80

Pseudosplines

Hastie, T. (1996) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1996.tb02088.x · Cited by 80

Interactive Cosegmentation Using Global and Local Energy Optimization

Dong, X., Shen, J., Shao, L., et al. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2456636 · Cited by 79

Penalized likelihood regression: General formulation and efficient approximation

Gu, C., & Kim, Y. (2002) · Canadian Journal of Statistics · doi:10.2307/3316100 · Cited by 79

Cross-modal coherent registration of whole mouse brains

Qu, L., Li, Y., Xie, P., Liu, L., Wang, Y., Wu, J., Liu, Y., Wang, T., Li, L., Guo, K., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01334-w · Cited by 73

Kernel-based covariate functional balancing for observational studies

Wong, R. K. W., & Chan, K. C. G. (2017) · Biometrika · doi:10.1093/biomet/asx069 · Cited by 79

Random rates in anisotropic regression (with a discussion and a rejoinder by the authors)

Hoffman, M., & Lepski, O. (2002) · The Annals of Statistics · doi:10.1214/aos/1021379858 · Cited by 79

Deciphering principles of transcription regulation in eukaryotic genomes

Nguyen, D., & D’haeseleer, P. (2006) · Molecular Systems Biology · doi:10.1038/msb4100054 · Cited by 81

Graph-Based Semi-Supervised Learning and Spectral Kernel Design

Johnson, R., & Zhang, T. (2008) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.911294 · Cited by 78

A stochastic deconvolution method to reconstruct insulin secretion rate after a glucose stimulus

Sparacino, G., & Cobelli, C. (1996) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.488799 · Cited by 76

Are bank shareholders enemies of regulators or a potential source of market discipline?

Park, S., & Peristiani, S. C. (2007) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2006.10.025 · Cited by 77

Nonlinear functional models for functional responses in reproducing kernel hilbert spaces

Lian, H. (2007) · Canadian Journal of Statistics · doi:10.1002/cjs.5550350410 · Cited by 77

Economic analysis of growth response from a pine plantation forest applied with biosolids

Kimberley, M. O., Wang, H., Wilks, P. J., et al. (2003) · Forest Ecology and Management · doi:10.1016/j.foreco.2003.09.003 · Cited by 77

Regression with Gaussian Processes

Williams, C. K. I. (1997) · Operations research, computer science. Interface series · doi:10.1007/978-1-4615-6099-9_66 · Cited by 77

Correlation Curves as Local Measures of Variance Explained by Regression

Doksum, K. A., Blyth, S., Bradlow, E. T., et al. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476782 · Cited by 77

Gemini Near Infrared Spectrograph Observations of the Central Supermassive Black Hole in Centaurus A

Silge, J. D., Gebhardt, K., Bergmann, M., et al. (2005) · The Astronomical Journal · doi:10.1086/431315 · Cited by 77

Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

Canatar, A., Bordelon, B., & Pehlevan, C. (2021) · RePEc: Research Papers in Economics · Cited by 75

Accurate emulators for large-scale computer experiments

Haaland, B., & Qian, P. Z. G. (2011) · The Annals of Statistics · doi:10.1214/11-aos929 · Cited by 76

Team MMSE Precoding With Applications to Cell-Free Massive MIMO

Miretti, L., Björnson, E., & Gesbert, D. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3147895 · Cited by 76

Mechanism for feature learning in neural networks and backpropagation-free machine learning models

Radhakrishnan, A., Beaglehole, D., Pandit, P., & Belkin, M. (2024) · Science · doi:10.1126/science.adi5639 · Cited by 100

Approximate real-time optimal control based on sparse Gaussian process models

Boedecker, J., Springenberg, J. T., Wülfing, J., & Riedmiller, M. (2014) · IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning · doi:10.1109/adprl.2014.7010608 · Cited by 74

Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation

Suzuki, T., & Sugiyama, M. (2012) · Neural Computation · doi:10.1162/neco_a_00407 · Cited by 76

Contextual occupancy maps using Gaussian processes

O’Callaghan, S., Ramos, F. T., & Durrant-Whyte, H. F. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152754 · Cited by 76

Regularization approach to inductive genetic programming

Nikolaev, N. Y., & Iba, H. (2001) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.942530 · Cited by 76

Technical Note: Comparing the effectiveness of recent algorithms to fill and smooth incomplete and noisy time series

Musiał, J. P., Verstraete, M. M., & Gobron, N. (2011) · Atmospheric chemistry and physics · doi:10.5194/acp-11-7905-2011 · Cited by 76

An implicit shift bidiagonalization algorithm for ill-posed systems

Björck, Å., Grimme, E., & Dooren, P. V. (1994) · BIT Numerical Mathematics · doi:10.1007/bf01934265 · Cited by 75

A hybrid segmental neural net/hidden Markov model system for continuous speech recognition

Zavaliagkos, G., Zhao, Y., Schwartz, R. M., et al. (1994) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.260358 · Cited by 75

Automated 3D Motion Tracking Using Gabor Filter Bank, Robust Point Matching, and Deformable Models

Chen, T., Wang, X., Chung, S., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2021041 · Cited by 74

Track deformable objects from point clouds with structure preserved registration

Tang, T., & Tomizuka, M. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919841431 · Cited by 73

Subspace regularization method for the single-trial estimation of evoked potentials

Karjalainen, P. A., Kaipio, J. P., Koistinen, A. S., et al. (1999) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.771195 · Cited by 74

Generalized Additive Model

Hastie, T. J., & Tibshirani, R. J. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a09018 · Cited by 73

3‐D inversion of gravity data in spherical coordinates with application to the GRAIL data

Liang, Q., Chen, C., & Li, Y. (2014) · Journal of Geophysical Research Planets · doi:10.1002/2014je004626 · Cited by 73

Clues to the cause of the 2011-2013 Campi Flegrei caldera unrest, Italy, from continuous GPS data

Amoruso, A., Crescentini, L., Sabbetta, I., et al. (2014) · Geophysical Research Letters · doi:10.1002/2014gl059539 · Cited by 73

A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning

Xu, J., Paiva, A. R. C., Park, I. M., et al. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.2005085 · Cited by 73

Flow Patterns and Morphological Changes in a Sandy Meander Bend during a Flood—Spatially and Temporally Intensive ADCP Measurement Approach

Kasvi, E., Laamanen, L., Lotsari, E., et al. (2017) · Water · doi:10.3390/w9020106 · Cited by 73

Using recursive algorithms for the efficient identification of smoothing spline ANOVA models

Ratto, M., & Pagano, A. J. (2010) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-010-0148-8 · Cited by 72

Gradient-Based Kernel Dimension Reduction for Regression

Fukumizu, K., & Leng, C. (2013) · Journal of the American Statistical Association · doi:10.1080/01621459.2013.838167 · Cited by 72

A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

Chen, X., Li, W., Chen, J., et al. (2014) · Remote Sensing · doi:10.3390/rs6042845 · Cited by 72

Reflectance sharing: predicting appearance from a sparse set of images of a known shape

Zickler, T. E., Ramamoorthi, R., Enrique, S., & Belhumeur, P. N. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.170 · Cited by 72

Optimal trajectory planning and smoothing splines

Egerstedt, M., & Martin, C. F. (2001) · Automatica · doi:10.1016/s0005-1098(01)00055-3 · Cited by 72

Structural and temporal analysis of the blogosphere through community factorization

Chi, Y., Shenghuo Zhu, B., Song, X., Tatemura, J., & Tseng, B. L. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281213 · Cited by 70

Penalized log-likelihood estimation for partly linear transformation models with current status data

Ma, S., & Kosorok, M. R. (2005) · The Annals of Statistics · doi:10.1214/009053605000000444 · Cited by 69

Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems

Escola, G. S., Fontanini, A., Katz, D. B., et al. (2011) · Neural Computation · doi:10.1162/neco_a_00118 · Cited by 71

Structured Functional Additive Regression in Reproducing Kernel Hilbert Spaces

Zhu, H., Yao, F., & Zhang, H. H. (2013) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12036 · Cited by 67

The Application of Machine Learning Algorithms to the Analysis of Electromyographic Patterns From Arthritic Patients

Nair, S. S., French, R. M., Laroche, D., & Thomas, E. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2032638 · Cited by 70

Recursive and en-bloc approaches to signal extraction

Young, P. C. (1999) · Journal of Applied Statistics · doi:10.1080/02664769922692 · Cited by 71

Validation of China-wide interpolated daily climate variables from 1960 to 2011

Yuan, W., Xu, B., Chen, Z., Xia, J., Xu, W., Chen, Y., Wu, X., & Fu, Y. (2014) · Theoretical and Applied Climatology · doi:10.1007/s00704-014-1140-0 · Cited by 69

Parameterizing state–space models for infectious disease dynamics by generalized profiling: measles in Ontario

Hooker, G., Ellner, S. P., Roditi, L. D. V., et al. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0412 · Cited by 70

A spatially explicit height–diameter model for Scots pine in Estonia

Schmidt, M., Kiviste, A., & von Gadow, K. (2010) · European Journal of Forest Research · doi:10.1007/s10342-010-0434-8 · Cited by 71

Kernel methods and the exponential family

Canu, S., & Smola, A. J. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.009 · Cited by 70

A sure-fired way to choose smoothing parameters in ill-conditioned inverse problems

Solo, V. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1996.560376 · Cited by 70

Analyzing accelerated degradation data by nonparametric regression

Shiau, J.-J. H., & Lin, H.-H. (1999) · IEEE Transactions on Reliability · doi:10.1109/24.784273 · Cited by 70

Multivariable Static Ankle Mechanical Impedance With Active Muscles

Lee, H., Ho, P., Rastgaar, M., et al. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2262689 · Cited by 70

Robust Feature Matching for Remote Sensing Image Registration via Linear Adaptive Filtering

Jiang, X., Ma, J., Fan, A., Xu, H., Lin, G., Lü, T., & Tian, X. S. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3001089 · Cited by 68

An introduction to the Hilbert-Schmidt SVD using iterated Brownian bridge kernels

Cavoretto, R., Fasshauer, G. E., & McCourt, M. (2014) · Numerical Algorithms · doi:10.1007/s11075-014-9850-z · Cited by 69

Modeling and Short-Term Forecasting of New South Wales Electricity System Load

Smith, M. S. (2000) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2000.10524885 · Cited by 69

Application of the wavelet transform to acoustic emission signals processing

Serrano, E. P., & Fabio, M. A. (1996) · IEEE Transactions on Signal Processing · doi:10.1109/78.502340 · Cited by 69

The Effect of Drinking Intensity and Frequency on Serum Carbohydrate‐Deficient Transferrin and γ‐ Glutamyl Transferase Levels in Outpatient Alcoholics

Anton, R. F., Stout, R. L., Roberts, J. S., & Allen, J. P. (1998) · Alcoholism Clinical and Experimental Research · doi:10.1111/j.1530-0277.1998.tb03935.x · Cited by 66

Glucose kinetics differ between women and men, during and after exercise

Horton, T. J., Grunwald, G. K., Lavely, J., & Donahoo, W. T. (2006) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01431.2005 · Cited by 68

Spline Regression in the Presence of Categorical Predictors

Ma, S., Racine, J. S., & Yang, L. (2014) · Journal of Applied Econometrics · doi:10.1002/jae.2410 · Cited by 68

Augmented Tikhonov regularization

Jin, B., & Zou, J. (2008) · Inverse Problems · doi:10.1088/0266-5611/25/2/025001 · Cited by 68

Information Theoretic Shape Matching

Hasanbelliu, E., Giraldo, L. S., & Príncipe, J. C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2324585 · Cited by 68

Dimensionality reduction on hyperspectral images: A comparative review based on artificial datas

Khodr, J., & Younès, R. (2011) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2011.6100531 · Cited by 68

The application of mean field theory to image motion estimation

Zhang, J., & Hanauer, G. G. (1995) · IEEE Transactions on Image Processing · doi:10.1109/83.350816 · Cited by 67

A Course in Morphometrics for Biologists

Bookstein, F. L. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108120418 · Cited by 66

Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens

González-Recio, Ó., Daniel Gianola, B., Rosa, G. J. M., WEIGEL, K. A., & Kranis, A. (2009) · Genetics Selection Evolution · doi:10.1186/1297-9686-41-3 · Cited by 66

Splines in Higher Order TV Regularization

Steidl, G., Didas, S., & Neumann, J. K. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-8066-7 · Cited by 67

Joint additive Kullback–Leibler residual minimization and regularization for linear inverse problems

Resmerita, E., & Anderssen, R. (2007) · Mathematical Methods in the Applied Sciences · doi:10.1002/mma.855 · Cited by 64

Vertisol Crack Extent Associated with Gilgai and Soil Moisture in the Texas Gulf Coast Prairie

Kishné, A. S., Morgan, C. L. S., & Miller, W. L. (2009) · Soil Science Society of America Journal · doi:10.2136/sssaj2008.0081 · Cited by 67

P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data

Wood, S. N. (2016) · Statistics and Computing · doi:10.1007/s11222-016-9666-x · Cited by 67

Negative association between type 1 diabetes and HLA DQB1*0602‐DQA1*0102 is attenuated with age at onset

Graham, J., Kockum, I. S., Sanjeevi, C. B., et al. (1999) · European Journal of Immunogenetics · doi:10.1111/j.1600-0609.2006.00789.x-i1 · Cited by 67

LASSO type penalized spline regression for binary data

Mullah, M. A. S., Hanley, J. A., & Benedetti, A. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01234-9 · Cited by 67

Online Learning as Stochastic Approximation of Regularization Paths: Optimality and Almost-Sure Convergence

Tarrès, P., & Yao, Y. (2014) · IEEE Transactions on Information Theory · doi:10.1109/tit.2014.2332531 · Cited by 66

Instantaneous deformation from continuous GPS: contributions from quasi-periodic loads

Bennett, R. A. (2008) · Geophysical Journal International · doi:10.1111/j.1365-246x.2008.03846.x · Cited by 66

Sparse Grids in a Nutshell

Garcke, J. (2012) · Lecture notes in computational science and engineering · doi:10.1007/978-3-642-31703-3_3 · Cited by 66

Use of ridge regression for improved estimation of kinetic constants from PET data

O’Sullivan, F., & Saha, A. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.759111 · Cited by 66

3-D inversion of magnetic data based on the L1–L2 norm regularization

Utsugi, M. (2019) · Earth Planets and Space · doi:10.1186/s40623-019-1052-4 · Cited by 65

Occurrence of demersal fishes in relation to near‐bottom oxygen levels within the C alifornia C urrent large marine ecosystem

Keller, A. A., Ciannelli, L., Wakefield, W. W., Simon, V., Barth, J. A., & Pierce, S. D. (2015) · Fisheries Oceanography · doi:10.1111/fog.12100 · Cited by 65

Optimal smoothing in nonparametric mixed-effect models

Gu, C., & Ma, P. (2005) · The Annals of Statistics · doi:10.1214/009053605000000110 · Cited by 66

Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies

Savitsky, T., Vannucci, M., & Sha, N. (2011) · Statistical Science · doi:10.1214/11-sts354 · Cited by 68

Bayesian learning of sparse classifiers

Figueiredo, M. A. T., & Jain, A. K. (2005) · 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.2001.990453 · Cited by 65

Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

Li, T., Zheng, X., Dai, Y., et al. (2014) · Advances in Atmospheric Sciences · doi:10.1007/s00376-014-3190-8 · Cited by 64

Testing the structural stability of temporally dependent functional observations and application to climate projections

Zhang, X., Shao, X., Hayhoe, K., et al. (2011) · Electronic Journal of Statistics · doi:10.1214/11-ejs655 · Cited by 64

An iterative method for Tikhonov regularization with a general linear regularization operator

Hochstenbach, M. E., & Reichel, L. (2010) · Journal of Integral Equations and Applications · doi:10.1216/jie-2010-22-3-465 · Cited by 64

Model selection for incomplete and design-based samples

Hens, N., Aerts, M., & Molenberghs, G. (2006) · Statistics in Medicine · doi:10.1002/sim.2559 · Cited by 64

Inferring genetic values for quantitative traits non-parametrically

Gianola, D., & Campos, G. D. L. (2008) · Genetics Research · doi:10.1017/s0016672308009890 · Cited by 63

Tutorial in biostatistics: spline smoothing with linear mixed models

Gurrin, L. C., Scurrah, K. J., & Hazelton, M. L. (2005) · Statistics in Medicine · doi:10.1002/sim.2193 · Cited by 63

Single-Image Super-Resolution via an Iterative Reproducing Kernel Hilbert Space Method

Deng, L.-J., Guo, W., & Huang, T. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2475895 · Cited by 64

Detecting Heteroscedasticity in Nonparametric Regression

Eubank, R. L., & Thomas, W. (1993) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1993.tb01474.x · Cited by 64

Nonparametric estimation of American options’ exercise boundaries and call prices

Broadie, M. N., Detemple, J. B., Ghysels, É., & Torrès, O. (2000) · Journal of Economic Dynamics and Control · doi:10.1016/s0165-1889(99)00094-9 · Cited by 57

Dynamic Classification of Traffic Flow Patterns Simulated by a Switching Multimode Discrete Cell Transmission Model

Çelikoğlu, H. B. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2317850 · Cited by 63

Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering

Çelikoğlu, H. B., & Silgu, M. A. (2016) · Transportation Science · doi:10.1287/trsc.2015.0653 · Cited by 63

A novel flexible framework with automatic feature correspondence optimization for nonrigid registration in radiotherapy

Osorio, E. M. V., Hoogeman, M. S., Bondar, L., Levendag, P. C., & Heijmen, B. J. M. (2009) · Medical Physics · doi:10.1118/1.3134242 · Cited by 62

Comparison of Spatial Interpolation Methods of Precipitation and Temperature Using Multiple Integration Periods

Hadi, S. J., & Tombul, M. (2018) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-018-0783-1 · Cited by 63

Medical Image Registration

Rueckert, D., & Schnabel, J. A. (2010) · Biological and medical physics series · doi:10.1007/978-3-642-15816-2_5 · Cited by 75

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