Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression
Maximum Likelihood Estimation of Effects in Least Angle Regression
Normal-Theory Maximum Likelihood Estimation of Beta Coefficients with ...
Augment calculations performed by eff.ridge() to prepare for display o...
Specify pairs of GRR Coefficient Estimates for display in Bivariate Co...
Efficient Maximum Likelihood (ML) Shrinkage via the Shortest Piecewise...
Internal RXshrink functions
m-Extents of Shrinkage used in eff.ridge() Calculations.
Calculate Bootstrap distribution of Unrestricted Maximum Likelihood (M...
Calculate Efficient Maximum Likelihood (ML) point-estimates for a Line...
Plot method for MLboot objects
Simulate data for Linear Models with known Parameter values and Normal...
Plot method for aug.lars objects
Plot method for eff.biv objects
Plot method for eff.ridge objects
Plot method for qm.ridge objects
Plot method for RXpredict objects
Plot method for syxi objects
Plot method for uc.lars objects
Plot method for YonX objects
Restricted (2-parameter) Maximum Likelihood Shrinkage in Regression
Predictions from Models fit using RXshrink Generalized Ridge Estimatio...
Maximum Likelihood (ML) Shrinkage using Generalized Ridge or Least Ang...
Linear and GAM Spline Predictions from a Single x-Variable
Maximum Likelihood Least Angle Regression on Uncorrelated X-Components
Maximum Likelihood (ML) Shrinkage in Simple Linear Regression
Functions are provided to calculate and display ridge TRACE Diagnostics for a variety of alternative Shrinkage Paths. While all methods focus on Maximum Likelihood estimation of unknown true effects under normal distribution-theory, some estimates are modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.) The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain (2022) <Open Statistics>. This "p-Parameter" Shrinkage-Path always passes through the vector of regression coefficient estimates Most-Likely to achieve the overall Optimal Variance-Bias Trade-Off and is the shortest Path with this property. Functions eff.aug() and eff.biv() augment the calculations made by eff.ridge() to provide plots of the bivariate confidence ellipses corresponding to any of the p*(p-1) possible ordered pairs of shrunken regression coefficients. Functions for plotting TRACE Diagnostics now have more options.