- 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 ... author presents a comprehensive treatment of penalty smoothing under a unified framework. Methods are developed ... construction, smoothing parameter selection, computation, and asymptotic convergence. Most of the computational and data analytical...
- Referenced in 978 articles
- smoothing refers to a general methodology for recovery of the underlying structure in data sets ... required for a comprehensive understanding of kernel smoothing, and hence smoothing problems in general...
- Referenced in 46 articles
- Data Sets from ”Forecasting with Exponential Smoothing”. Data sets from the book ”Forecasting with exponential...
- Referenced in 142 articles
- Fortran subroutines for calculating smoothing splines for various kinds of data and geometries, with automatic...
- Referenced in 76 articles
- curves. In the use of smoothing methods in data analysis, an important question is which...
- Referenced in 71 articles
- linked to the book ’Applied Smoothing Techniques for Data Analysis - The Kernel Approach with...
- Referenced in 67 articles
- Smooth functions and local extreme values. Computational Statistics and Data Analysis (to appear) D”umbgen ... Extensions of smoothing via taut strings Davies, P. L. (1995) Data features. Statistica Neerlandica...
- Referenced in 426 articles
- Plus. A library of functions and data written by Angelo Canty to accompany the Cambridge ... basic ideas to several samples, semiparametric and smooth models. Significance and confidence intervals ... nonlinear models, and time series, spatial data and point processes. Chapter 9 describes how variance...
- Referenced in 458 articles
- both. The 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 142 articles
- spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns ... 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 14 articles
- MortalitySmooth package provides a framework for smoothing count data in both one- and two-dimensional ... using a practical tool for smoothing mortality data over ages and/or years. The total number...
- Referenced in 21 articles
- methods for the selection of the data-driven smoothing parameter are also provided...
- Referenced in 13 articles
- calculations in data analysis and data smoothing including ridge regression, thin plate smoothing splines, deconvolution...
- Referenced in 344 articles
- Data for ”GAMs: An Introduction with R”. Data sets and scripts used in the book ... accessible and the author used a fairly smooth way even in the case of advanced...
- Referenced in 48 articles
- package ks Kernel Smoothing: Kernel smoothers for univariate and multivariate data, including density functions, density...
- Referenced in 73 articles
- smoothness is crucial for mathematical theory as well as performance for finite-sample data ... Finally, an adaptive version of our sparsity-smoothness penalized approach yields large additional performance gains...
- 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 133 articles
- 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...
- Referenced in 56 articles
- splines, Kriging and compact covariances for large data sets. The splines and Kriging methods ... supporting by functions that can determine the smoothing parameter (nugget and sill variance) by cross ... functions for plotting and working with spatial data as images. This package also contains...
- Referenced in 32 articles
- auto-correlation and smoothing terms, user defined dependence structures, censored data, meta-analytic standard errors...