The optimality of iterative maximum penalized likelihood algorithms for ridge regression

DOI10.1016/0893-9659(94)90007-8zbMath0799.62075OpenAlexW2012962545MaRDI QIDQ1334819

V. Pereyra

Publication date: 22 September 1994

Published in: Applied Mathematics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0893-9659(94)90007-8

zbMATH Keywords

EM algorithmridge regressionlinear stationary methods of the first degreeone-step-late algorithmoptimum extrapolated methodsOSL algorithms

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Probabilistic methods, stochastic differential equations (65C99)

Related Items (1)

N-Stage splitting for maximum penalized likelihood estimation with gaussian data and stationary linear iterative methods

Cites Work

• Matrix Analysis of a Two-Stage-Splitting Iteration for Maximum Penalized Likelihood Estimation
• Ridge Regression: Applications to Nonorthogonal Problems
• Nonparametric Roughness Penalties for Probability Densities
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