- Referenced in 401 articles
- SemiPar: Semiparametic Regression. The primary aim of this book is to guide researchers needing ... flexibly incorporate nonlinear relations into their regression analyses. Almost all existing regression texts treat either ... parametric or nonparametric regression exclusively. In this book the authors argue that nonparametric regression ... relatively simple extension of parametric regression and treat the two together. They refer to this...
- Referenced in 632 articles
- nonparametric curve estimation setting, namely density and regression estimation. Special attention is given...
- Referenced in 145 articles
- elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression...
- Referenced in 162 articles
- alr3: Data to accompany Applied Linear Regression 3rd edition , This package is a companion ... textbook S. Weisberg (2005), ”Applied Linear Regression,” 3rd edition, Wiley. It includes all the data...
- Referenced in 223 articles
- pattern recognition, for the problem of regression, and for the problem of learning a ranking...
- Referenced in 216 articles
- analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics...
- Referenced in 151 articles
- unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses ... popular S/S- PLUS language. Code for regression has been distributed in the R package...
- Referenced in 189 articles
- builds across multiple platforms, generating documentation and regression testing across a set of target platforms...
- Referenced in 109 articles
- procedure, based on a principle of ordinal regression, consist in solving a small linear program ... sensitivity analysis of the ordinal regression problem. There is a friendly user interface in which...
- Referenced in 129 articles
- statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric...
- Referenced in 110 articles
- nonconvex. It is also effective for nonlinear regression, problems with complementarity constraints (MPCCs or MPECs...
- Referenced in 57 articles
- SVMTorch: Support vector machines for large-scale regression problems. Support Vector Machines (SVMs) for regression ... classification problems, but adapted to regression problems. With this algorithm, one can now efficiently solve ... large-scale regression problems (more than 20000 examples). Comparisons with Nodelib, another publicly available ... algorithm for large-scale regression problems from G. Flake and S. Lawrence [Mach. Learn...
- Referenced in 60 articles
- Ordinal regression revisited: multiple criteria ranking with a set of additive value functions. VisualUTA ... value functions which result from an ordinal regression. The preference information provided by the decision ... alternatives. The preference model built via ordinal regression is a set of all additive value...
- Referenced in 64 articles
- CAViaR: Conditional autoregressive value at risk by regression quantiles. Value at risk ... autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period...
- Referenced in 60 articles
- ftnonpar: Features and Strings for Nonparametric Regression. The package contains R-functions to perform ... methods in nonparametric regression and density estimation, described in Davies, P. L. and Kovac...
- Referenced in 82 articles
- model representations, such as linear and nonlinear regression models and generalized linear models. Many aspects...
- Referenced in 78 articles
- Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary...
- Referenced in 51 articles
- Cutler’s random forests for classification and regression. Classification and regression based on a forest...
- Referenced in 33 articles
- Regression Modeling Strategies , Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced ... contains functions for binary and ordinal logistic regression models and the Buckley-James multiple regression ... linear models. rms works with almost any regression model, but it was especially written ... work with binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear...
- Referenced in 62 articles
- large-scale linear classification. It supports logistic regression and linear support vector machines. We provide...