- Referenced in 223 articles
- scores of methods have proved successful for univariate smoothing, ones practical in multivariate settings number ... smoothing methods derived through roughness penalties that are suitable for both univariate and multivariate problems ... penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian ... general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions...
- Referenced in 321 articles
- with robust methods. This includes regression methodology including model selections and multivariate statistics where...
- Referenced in 208 articles
- statistical methods for data fitting and diagnostics, in the univariate and the multivariate case...
- Referenced in 351 articles
- validating algorithms. It comprises of self-validating methods for dense linear systems (also inner inclusions ... including unconstrained optimization) roots of univariate and multivariate nonlinear equations (simple and clusters) eigenvalue problems...
- Referenced in 25 articles
- package MNM: Multivariate Nonparametric Methods. An Approach Based on Spatial Signs and Ranks , The package ... provides multivariate tests, estimates and methods based on the identity score, spatial sign score...
- Referenced in 41 articles
- Ecological Data : Exploratory and Euclidean methods in Environmental sciences , Multivariate data analysis and graphical display...
- Referenced in 23 articles
- Least Squares and Principal Component regression , Multivariate regression methods Partial Least Squares Regression (PLSR), Principal...
- Referenced in 253 articles
- spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models. Scientists ... computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem ... process models for univariate as well as multivariate point-referenced data. We discuss the algorithms...
- Referenced in 26 articles
- analysis, both for univariate and multivariate data. The main methods and models are illustrated with ... reminded, and Markov chain Monte Carlo methods are presented. Chapter 2 is on dynamic linear ... series analysis are investigated, both univariate and multivariate. Chapter 4 covers models with unknown parameters ... last chapter is on sequential Monte Carlo methods...
- Referenced in 94 articles
- carefully designed and easily extensible platform for multivariate modeling with copulas in R. S4 classes ... copulas and Archimedean copulas are implemented, with methods for density/distribution evaluation, random number generation...
- Referenced in 107 articles
- obtaining, A C++ implementation of statistical methods in A. Ronald Gallant’s, ”Nonlinear Statistical Models ... generalized least squares estimates for a multivariate nonlinear regression model. Use the SUR option ... dynamic systems, possibly in implicit form, generalized method-of-moments estimates, GMM option...
- Referenced in 49 articles
- package copula: Multivariate Dependence with Copulas. Classes (S4) of commonly used elliptical, Archimedean, extreme value ... some more copula families. Methods for density, distribution, random number generation, bivariate dependence measures, perspective ... variance estimates. Independence and serial (univariate and multivariate) independence tests, and other copula related tests...
- Referenced in 19 articles
- subspace-searching simplex method for the unconstrained optimization of general multivariate functions. Like the Nelder...
- Referenced in 26 articles
- Newton Raphson Algorithm (ml with method d0). In the case of discrete random effects ... used for continuous (multivariate) normal random effects or factors. Two methods are available for numerical...
- Referenced in 18 articles
- contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization Regression analysis ... widely used statistical method for modelling relationships between variables. Multivariate adaptive regression splines (MARS) especially ... high-dimensional problems and fitting nonlinear multivariate functions. A special advantage of MARS lies ... determine the response variable. The MARS method consists of two parts: forward and backward algorithms...
- Referenced in 10 articles
- structure of the missing values, the corresponding methods may help to identify the mechanism generating ... using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface allows...
- Referenced in 85 articles
- Evolution strategies (ES) are stochastic, derivative-free methods for numerical optimization of non-linear ... candidate solutions are sampled according to a multivariate normal distribution in the mathbb ... covariance matrix adaptation (CMA) is a method to update the covariance matrix of this distribution...
- Referenced in 7 articles
- presents a practical and simple fully nonparametric multivariate smoothing procedure that adapts to the underlying ... real example, the Boston Housing Data, our method reduces the out of sample prediction error ... available at CRAN, implements the proposed multivariate nonparametric method...
- Referenced in 3 articles
- software SAM II. Sensitivity analysis in mutivariate methods. The statistical software SAM II which ... designed to make sensitivity analysis in descriptive multivariate analysis. This program has the unified ... formulation of sensitivity analysis for any multivariate methods formulated as eigenvalue problems by using generalized ... this revision, the number of multivariate methods to be treated are increased, the displays...
- Referenced in 6 articles
- Guerry: Maps, data and methods related to Guerry (1833) ”Moral Statistics of France”. This package ... multivariate data from A.-M. Guerry and others, and statistical and graphic methods related ... development of statistical and graphic methods for multivariate data in a geo-spatial context...