- Referenced in 293 articles
- Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several ... computing power, smoothing methods are now finding their ways into everyday data analysis by practitioners ... comprehensive treatment of penalty smoothing under a unified framework. Methods are developed for (i) regression ... time data and covariates. The unifying themes are the general penalized likelihood method...
- Referenced in 76 articles
- curves. In the use of smoothing methods in data analysis, an important question is which...
- Referenced in 458 articles
- nonlinear functions must be smooth. Stable numerical methods are employed throughout. Features include ... efficiently. File formats for constraint and basis data are compatible with the industry MPS format...
- Referenced in 426 articles
- data written by Angelo Canty to accompany the Cambridge University Press publication Bootstrap Methods ... Davison and David Hinkley. Bootstrap methods are computer-intensive methods of statistical analysis using simulation ... date coverage of bootstrap methods with numerous applied examples, together with the basic theory without ... Chapter 2 describes the properties of resampling methods for use with single samples from parametric...
- Referenced in 344 articles
- Data for ”GAMs: An Introduction with R”. Data sets and scripts used in the book ... explanations of the theory underlying the statistical methods and material on statistical modelling ... accessible and the author used a fairly smooth way even in the case of advanced...
- Referenced in 142 articles
- spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns ... spatial windows, pixel images and tessellations. Exploratory methods include K-functions, nearest neighbour distance ... statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated ... models can be fitted to point pattern data using functions ppm, kppm, slrm similar...
- Referenced in 71 articles
- package sm: Smoothing methods for nonparametric regression and density estimation. This is software ... linked to the book ’Applied Smoothing Techniques for Data Analysis - The Kernel Approach with...
- Referenced in 9 articles
- CircSiZer: an exploratory tool for circular data. Smoothing methods and SiZer (SIgnificant ZERo crossing ... tools for exploring significant underlying structures in data samples. An extension of SiZer to circular...
- Referenced in 56 articles
- compact covariances for large data sets. The splines and Kriging methods are supporting by functions ... that can determine the smoothing parameter (nugget and sill variance) by cross validation and also ... spatial data as images. This package also contains an implementation of sparse matrix methods...
- Referenced in 133 articles
- NESTA: A fast and accurate first-order method for sparse recovery. Accurate signal recovery ... image reconstruction from indirect and possibly undersampled data is a topic of considerable interest ... already quite immense. This paper applies a smoothing technique and an accelerated first-order algorithm ... with recently proposed state-of-the-art methods. We also apply the algorithm to solve...
- Referenced in 12 articles
- Methods. This package implements nonparametric smoothing methods for circular data...
- Referenced in 42 articles
- Efficient Projections. Main Features: 1) First-Order Method. At each iteration, we only need ... data. 2) Optimal Convergence Rate. The convergence rate O(1/k2) is optimal for smooth convex ... optimization via the first-order black-box methods. 3) Efficient Projection. The projection problem (proximal...
- Referenced in 67 articles
- package contains R-functions to perform the methods in nonparametric regression and density estimation, described ... Smooth functions and local extreme values. Computational Statistics and Data Analysis (to appear) D”umbgen...
- Referenced in 21 articles
- kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also...
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- Method for Big Data Problems. Recent advances in optimization theory have shown that smooth strongly ... this work we introduce a new method in this class with a theoretical convergence rate...
- Referenced in 46 articles
- with Exponential Smoothing”. Data sets from the book ”Forecasting with exponential smoothing: the state space ... Forecasting with exponential smoothing. The state space approach. Exponential smoothing methods have been around since...
- Referenced in 72 articles
- Monte Carlo-like approach known as smoothed particle hydrodynamics (SPH). Unlike most previous implementations ... unification of SPH with the hierarchical tree method is a natural way of allowing ... larger N within a Lagrangian framework. The data structures used to manipulate the grouping...
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- ODEs. Numerical experiments with both smooth and nonsmooth data support the efficiency and accuracy ... compared to other well-known second-order methods such as RKC and ROCK2...
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- smooth curve using penalized spline estimators. We consider three types of estimation methods ... formula is applied for the first two methods and compared with Bayesian simultaneous confidence bands ... either Bayesian or frequentist properties. Simulations and data analysis support the proposed methods...
- Referenced in 7 articles
- front. Other methods that can reduce tangles and crossings are “smoothing” of data and parameters ... front. Finally, we investigate the level set method as a new tool for forest fire...