• SemiPar

  • Referenced in 761 articles [sw07116]
  • based on penalized regression splines and mixed models. Every model in this book ... special case of the linear mixed model or its generalized counterpart. This book is very ... from their collaborative research have driven the selection of material and emphases and are used...
  • gss

  • Referenced in 317 articles [sw06099]
  • general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions ... Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence. Most...
  • quantreg

  • Referenced in 162 articles [sw04356]
  • parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response ... methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk...
  • CAPUSHE

  • Referenced in 62 articles [sw13365]
  • Slope heuristics: overview and implementation. Model selection is a general paradigm which includes many statistical ... minimization of a penalized criterion. L. Birgé and P. Massart [Probab. Theory Relat. Fields...
  • capushe

  • Referenced in 5 articles [sw28004]
  • Using Slope HEuristics. Calibration of penalized criteria for model selection. The calibration methods available...
  • glmmLasso

  • Referenced in 8 articles [sw14724]
  • glmmLasso: Variable Selection for Generalized Linear Mixed Models by L1-penalized Estimation. R package...
  • ppls

  • Referenced in 5 articles [sw24174]
  • Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence...
  • OSCAR

  • Referenced in 58 articles [sw03026]
  • similar behavior. The technique is based on penalized least squares with a geometrically intuitive penalty ... variable selection techniques in terms of both prediction error and model complexity, while yielding...
  • qrcmNP

  • Referenced in 2 articles [sw35602]
  • penalized approach to covariate selection through quantile regression coefficient models. The coefficients of a quantile ... time. Another possibility is to model the coefficient functions parametrically, an approach that is referred ... penalized method that can address the selection of covariates in this particular modelling ... framework. Unlike standard penalized quantile regression estimators, in which model selection is quantile-specific...
  • QICD

  • Referenced in 20 articles [sw19679]
  • Coefficients for Non-Convex Penalized Quantile Regression Model by using QICD Algorithm. Extremely fast algorithm ... Coordinate Descent Algorithm for High-dimensional Nonconvex Penalized Quantile Regression. This algorithm combines the coordinate ... median, which ensures fast computation. Tuning parameter selection is based on two different method ... cross validation and BIC for quantile regression model. Details are described in Peng...
  • MixMoGenD

  • Referenced in 6 articles [sw08624]
  • model selection problem. The competing models are compared using penalized maximum likelihood criteria. Under weak ... associated algorithm named mixture model for genotype data (MixMoGenD) has been implemented using C++ programming ... avoid an exhaustive search of the optimum model, we propose a modified Backward-Stepwise algorithm ... enables a better search of the optimum model among all possible cardinalities...
  • crrp

  • Referenced in 2 articles [sw28755]
  • package crrp: Penalized Variable Selection in Competing Risks Regression. In competing risks regression, the proportional ... subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects ... This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD...
  • Plus

  • Referenced in 3 articles [sw08189]
  • Penalized Linear Unbiased Selection. Efficient procedures for fitting an entire regression sequences with different model...
  • scam

  • Referenced in 7 articles [sw15787]
  • Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with ... multiple smoothing parameter selection by GCV or UBRE/AIC...
  • cntclust

  • Referenced in 7 articles [sw40587]
  • mild and gross outliers. We propose a model-based clustering procedure where each component ... trimmed. We propose a penalized likelihood approach for estimation and selection of the proportions ... without trimming, and over trimmed normal mixture models ( exttt{tclust}). We conclude with an original...
  • HurdleNormal

  • Referenced in 1 article [sw30708]
  • estimate graphical models using group-lasso penalized neighborhood selection...
  • pi-MASS

  • Referenced in 20 articles [sw17249]
  • naturally be cast as a variable selection regression problem, with the SNPs as the covariates ... model, which we argue is a strength of BVSR compared with alternatives such as penalized...
  • cvplogistic

  • Referenced in 5 articles [sw29394]
  • solution surface for concave penalized logistic regression model. The SCAD and MCP (default ... Lasso-concave hybrid penalty for fast variable selection. The hybrid penalty applies the concave penalty...
  • lassopack

  • Referenced in 1 article [sw37548]
  • lassopack: Model selection and prediction with regularized regression in Stata. In this article, we introduce ... offer three approaches for selecting the penalization (“tuning”) parameters: information criteria (implemented in lasso2...
  • rope

  • Referenced in 1 article [sw21810]
  • Selected Variables. Selects one model with variable selection FDR controlled at a specified level ... variable selection counts over many bootstraps for several levels of penalization, is modeled as coming...