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1. Full Text Spatial data analysis in ecology and agriculture using R
by Plant, Richard E
2012, ISBN 9781439819135, xv, 631
Assuming no prior knowledge of R, Spatial Data Analysis in Ecology and Agriculture Using R provides practical instruction on the use of the R programming... 
Spatial analysis (Statistics) | R (Langage de programmation) | ecology | Méthodes statistiques | Analyse spatiale (Statistique) | Agriculture | R (Computer program language) | Statistical methods | statistical analysis | computers
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2. Density estimation for statistics and data analysis
by Silverman, B. W., 1952
1986, Monographs on statistics and applied probability (Series), ISBN 0412246201, ix, 175
Estimation theory
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3. Full Text Density Estimation
by Simon J. Sheather
Statistical Science, ISSN 0883-4237, 11/2004, Volume 19, Issue 4, pp. 588 - 597
This paper provides a practical description of density estimation based on kernel methods. An important aim is to encourage practicing statisticians to apply... 
Datasets | Density estimation | Histograms | Estimation bias | Wands | Least squares | Data smoothing | Estimators | Point estimators | Estimation methods | Local likelihood density estimates | Bandwidth selection | Kernel density estimation | Data sharpening | kernel density estimation | local likelihood density estimates | KERNEL | bandwidth selection | SQUARES CROSS-VALIDATION | HISTOGRAMS | STATISTICS & PROBABILITY | data sharpening
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4. Full Text KERNEL DENSITY ESTIMATION VIA DIFFUSION
by Z. I. Botev and J. F. Grotowski and D. P. Kroese
The Annals of Statistics, ISSN 0090-5364, 10/2010, Volume 38, Issue 5, pp. 2916 - 2957
We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing... 
Density estimation | Estimation bias | Sample size | Random variables | Data smoothing | Diffusion coefficient | Estimators | Point estimators | Estimation methods | Bandwidth selection | Heat kernel | Nonparametric density estimation | Diffusion equation | Langevin process | Boundary bias | Data sharpening | Variable bandwidth | Normal reference rules | variable bandwidth | normal reference rules | BIAS | STATISTICS & PROBABILITY | BOUNDARY CORRECTION | boundary bias | data sharpening | PLUG-IN | heat kernel | bandwidth selection | VARIABLE LOCATION | diffusion equation | 62G07 | 60J60 | 60J70 | 35K05 | 35K15 | 62G20
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5. Full Text Robust kernel density estimation
by Kim, JooSeuk and Scott, Clayton D
Journal of Machine Learning Research, ISSN 1532-4435, 09/2012, Volume 13, pp. 2529 - 2565
We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by... 
Outlier | Influence function | Reproducing kernel Hilbert space | Kernel trick | M-estimation | REGRESSION | kernel trick | outlier | reproducing kernel Hilbert space | influence function | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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6. Full Text Maximum Correntropy Estimation Is a Smoothed MAP Estimation
by Badong Chen and Principe, J. C
IEEE Signal Processing Letters, ISSN 1070-9908, 08/2012, Volume 19, Issue 8, pp. 491 - 494
As a new measure of similarity, the correntropy can be used as an objective function for many applications. In this letter, we study Bayesian estimation under... 
Correntropy | Smoothing methods | Convolution | Estimation | Mean square error methods | Probability density function | maximum a posteriori estimation | Random variables | Kernel | maximum correntropy estimation | estimation | ENGINEERING, ELECTRICAL & ELECTRONIC
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7. Full Text Minimum bias multiple taper spectral estimation
by Riedel, K.S and Sidorenko, A
IEEE Transactions on Signal Processing, ISSN 1053-587X, 01/1995, Volume 43, Issue 1, pp. 188 - 195
Two families of orthonormal tapers are proposed for multitaper spectral analysis: minimum bias tapers, and sinusoidal tapers {/spl upsisup (k/)}, where /spl... 
Fourier transforms | Error analysis | Discrete Fourier transforms | Bandwidth | Frequency | Eigenvalues and eigenfunctions | Spectral analysis | KERNEL | ENGINEERING, ELECTRICAL & ELECTRONIC | Observations | Spectrum analysis | Time-series analysis
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8. Full Text Kernel methods for software effort estimation
: Effects of different kernel functions and bandwidths on estimation accuracy
by Kocaguneli, Ekrem and Menzies, Tim and Keung, Jacky W
Empirical Software Engineering, ISSN 1382-3256, 2/2013, Volume 18, Issue 1, pp. 1 - 24
Analogy based estimation (ABE) generates an effort estimate for a new software project through adaptation of similar past projects (a.k.a. analogies). Majority... 
Kernel function | Programming Languages, Compilers, Interpreters | Computer Science | Bandwidth | Software Engineering/Programming and Operating Systems | Effort estimation | Data mining | COMPUTER SCIENCE, SOFTWARE ENGINEERING | EFFORT PREDICTION | SIMULATION | SELECTION | COST ESTIMATION MODELS | ANALOGY | Computer science | Electrical engineering | Software | Internet software | Methods | Kernels | Analogies | Weighting | Density | Computer programs | Adaptation
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9. Full Text Nonparametric quantile estimation
by Takeuchi, Ichiro and Le, Quoc V and Sears, Timothy D and Smola, Alexander J
Journal of Machine Learning Research, ISSN 1533-7928, 07/2006, Volume 7, pp. 1231 - 1264
In regression, the desired estimate of y vertical bar x is not always given by a conditional mean, although this is most common. Sometimes one wants to obtain... 
Support vector machines | Kernel methods | Estimation with constraints | Quantile estimation | Nonparametric techniques | REGRESSION | MACHINES | quantile estimation | support vector machines | APPROXIMATION | nonparametric techniques | SUPPORT VECTOR | SMOOTHING SPLINES | kernel methods | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | estimation with constraints
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10. Full Text Strictly Proper Scoring Rules, Prediction, and Estimation
by Gneiting, Tilmann and Raftery, Adrian E
Journal of the American Statistical Association, ISSN 0162-1459, 03/2007, Volume 102, Issue 477, pp. 359 - 378
Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that... 
Continuous ranked probability score | Strictly proper | Predictive distribution | Utility function | Bayes factor | Entropy | Skill score | Brier score | Prediction interval | Minimum contrast estimation | Loss function | Scoring rule | Bregman divergence | Cross-validation | Quantile forecast | Kernel score | Negative definite function | Coherent | Weather | Maximum likelihood estimation | Forecasting standards | Review Article | Weather forecasting | Statistical weather forecasting | Probability forecasts | Interval estimators | Probabilities | Forecasting models | kernel score | coherent | PRECIPITATION FORECASTS | loss function | entropy | predictive distribution | MODEL SELECTION | quantile forecast | INTERVAL ESTIMATION | minimum contrast estimation | REGRESSION | INFORMATION | ENSEMBLES | STATISTICS & PROBABILITY | cross-validation | utility function | GENERAL-METHOD | PROBABILITY-DISTRIBUTIONS | prediction interval | brier score | negative definite function | strictly proper | scoring rule | SYSTEMS | continuous ranked probability score | skill score | VERIFICATION | Bayesian statistical decision theory | Prediction theory | Usage | Analysis
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11. Nonparametric function estimation, modeling, and simulation
by Thompson, James R and Tapia, Richard A and Thompson, James R and Society for Industrial and Applied Mathematics
1990, ISBN 9780898712612, xvi, 304
Mathematical models | Statistical analysis | Statistical models | Simulation | Nonparametric statistics | Estimation theory
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12. Full Text Super-Resolution Without Explicit Subpixel Motion Estimation
by Takeda, H and Milanfar, P and Protter, M and Elad, M
IEEE Transactions on Image Processing, ISSN 1057-7149, 09/2009, Volume 18, Issue 9, pp. 1958 - 1975
The need for precise (subpixel accuracy) motion estimates in conventional super-resolution has limited its applicability to only video sequences with... 
Optical filters | Multidimensional systems | Motion estimation | spatially adaptive | Video sequences | nonlinear filter | super-resolution | Displays | local polynomial | nonparametric | Denoising | Interpolation | regression | Spatiotemporal phenomena | Spatial resolution | Kernel | Nonlinear optics | frame rate upconversion | Frame rate upconversion | Spatially adaptive | Nonlinear filter | Nonparametric | Super-resolution | Regression | Local polynomial | kernel | RECONSTRUCTION | ALGORITHM | RESTORATION | LEAST-SQUARES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | PERFORMANCE ANALYSIS | interpolation | IMAGE | ROBUST ESTIMATION | Evaluation | Usage | Resolution (Optics) | Image processing | Kernel functions | Analysis | Kernels | Approximation | Rendering | Least squares method | Mathematical analysis | Series (mathematics) | Pixels
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13. Full Text Defocus map estimation from a single image
by Zhuo, Shaojie and Sim, Terence
Pattern Recognition, ISSN 0031-3203, 2011, Volume 44, Issue 9, pp. 1852 - 1858
In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the... 
Gaussian gradient | Image processing | Defocus blur | Defocus magnification | Defocus map | SHAPE | DEPTH | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Kernels | Recovering | Propagation | Images | Gaussian | Pattern recognition | Estimates | Ambiguity
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14. Full Text Density-Difference Estimation
by Sugiyama, Masashi and Kanamori, Takafumi and Suzuki, Taiji and Plessis, Marthinus Christoffel du and Liu, Song and Takeuchi, Ichiro
Neural Computation, ISSN 1530-888X, 10/2013, Volume 25, Issue 10, pp. 2734 - 2775
We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two... 
Letters | COVARIATE SHIFT ADAPTATION | CLASSIFIER | DIVERGENCE ESTIMATION | TIME-SERIES DATA | ALGORITHM | CHANGE-POINT DETECTION | ERROR | DATA-DEPENDENT PARTITIONS | NEUROSCIENCES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | RATIO ESTIMATION | KERNELS | Algorithms | Artificial Intelligence | Diabetes Mellitus - epidemiology | Humans | Data Mining - statistics & numerical data | Logistic Models | Software | Australia | Germany | Databases, Factual | Approximation
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15. Full Text Information-Theoretic Compressive Sensing Kernel Optimization and Bayesian Cramér–Rao Bound for Time Delay Estimation
by Yujie Gu and Goodman, Nathan A
IEEE Transactions on Signal Processing, ISSN 1053-587X, 09/2017, Volume 65, Issue 17, pp. 4525 - 4537
With the adoption of arbitrary and increasingly wideband signals, the design of modern radar systems continues to be limited by analog-to-digital converter... 
Delay effects | Compressive sensing (CS) | Estimation | Gaussian mixture (GM) | time delay estimation | minimum mean-square error (MMSE) | Analog-digital conversion | Radar | sensing kernel optimization | Sensors | Cramér–Rao bound (CRB) | mutual information | Kernel | Compressed sensing | Cramér-Rao bound (CRB) | DESIGN | Cramer-Rao bound (CRB) | REFLECTIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | RECOVERY | ERROR | RANGE
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16. Full Text Blind Image Blur Estimation via Deep Learning
by Ruomei Yan and Ling Shao
IEEE Transactions on Image Processing, ISSN 1057-7149, 04/2016, Volume 25, Issue 4, pp. 1910 - 1921
Image blur kernel estimation is critical to blind image deblurring. Most existing approaches exploit handcrafted blur features that are optimized for a certain... 
Training | Parameter estimation | blur parameter estimation | Image edge detection | blind image deblurring | Neural networks | Estimation | Feature extraction | blur classification | Kernel | DECONVOLUTION | Blur classification | EXPERTS | general regression neural network | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Learning | Usage | Analysis | Research | Edge detection (Image processing) | Regression analysis
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17. Full Text Estimation of environmental exposure: interpolation, kernel density estimation or snapshotting
by Shi, Xun and Li, Meifang and Hunter, Olivia and Guetti, Bart and Andrew, Angeline and Stommel, Elijah and Bradley, Walter and Karagas, Margaret
Annals of GIS, ISSN 1947-5683, 01/2019, Volume 25, Issue 1, pp. 1 - 8
In environmental health researches and practices, spatial analysis became an important approach to estimation of environmental exposure of human subjects under... 
Environmental exposure | health | interpolation | kernel density estimation | snapshotting
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18. Full Text Variational Dirichlet Blur Kernel Estimation
by Xu Zhou and Mateos, Javier and Fugen Zhou and Molina, Rafael and Katsaggelos, Aggelos K and Northwestern Univ., Evanston, IL (United States). Center for Catalysis and Surface Science
IEEE Transactions on Image Processing, ISSN 1057-7149, 12/2015, Volume 24, Issue 12, pp. 5127 - 5139
Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution... 
inverse problem | Image edge detection | Estimation | image deblurring | Blind deconvolution | Approximation methods | Dirichlet distribution | point spread function | Deconvolution | Cost function | variational distribution approximations | Kernel | constrained optimization | IMAGE | CAMERA | RESTORATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Bayesian analysis | Kernels | Algorithms | Approximation | Mathematical analysis | Images | Dirichlet problem | Blinds | MATHEMATICS AND COMPUTING | blind deconvolution
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19. Full Text Direct density derivative estimation
by Sasaki, Hiroaki and Noh, Yung-Kyun and Niu, Gang and Sugiyama, Masashi
Neural Computation, ISSN 0899-7667, 06/2016, Volume 28, Issue 6, pp. 1101 - 1140
Estimating the derivatives of probability density functions is an essential step in statistical data analysis. A naive approach to estimate the derivatives is... 
CHOICE | KERNEL | ROBUST | CROSS-VALIDATION | RATIO | GRADIENT | NEUROSCIENCES | MEAN SHIFT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Multidimensional data | Estimating | Derivatives | Computational efficiency | Estimates | Density | Estimators | Tuning
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20. Full Text CONFIDENCE BANDS IN DENSITY ESTIMATION
by Evarist Giné and Richard Nickl
The Annals of Statistics, ISSN 0090-5364, 4/2010, Volume 38, Issue 2, pp. 1122 - 1170
Given a sample from some unknown continuous density $f\colon {\Bbb R}\rightarrow {\Bbb R}$ , we construct adaptive confidence bands that are honest for all... 
Integers | Density estimation | Minimax | Covariance | Mathematical functions | Random variables | Density | Mathematical maxima | Estimators | Confidence interval | Wavelet estimators | Kernel estimators | Extremes | Gaussian processes | Adaptive estimation | Limit theorem | INEQUALITIES | APPROXIMATION | NONPARAMETRIC REGRESSION | STATISTICS & PROBABILITY | extremes | limit theorem | RATES | GAUSSIAN-PROCESSES | kernel estimators | SETS | ADAPTATION | REGIONS | CONVERGENCE | INTERVALS | density estimation | wavelet estimators | 62G07 | 60F05
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