Institute of Mathematical Statistics Lecture Notes - Monograph Series

Mixed Models, Posterior Means and Penalized Least-Squares

Yolanda Muñoz Maldonado

Full-text: Open access

Abstract

This paper reviews the connections between estimators that derive from three different modeling methodologies: Mixed-effects models, Bayesian models and Penalized Least-squares. Extension of classical results on the equivalence for smoothing spline estimators and best linear unbiased prediction and/or posterior analysis of certain Gaussian signal-plus-noise models is examined in a more general setting. These connections allow for the application of an efficient, linear time algorithm, to estimate parameters, compute random effects predictions and evaluate likelihoods in a large class of model scenarios. We also show that the methods of generalized cross-validation, restricted maximum likelihood and unbiased risk prediction can be used to estimate the variance components or adaptively select the smoothing parameters in any of the three settings.

Chapter information

Source
Javier Rojo, ed., Optimality: The Third Erich L. Lehmann Symposium (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2009), 216-236

Dates
First available in Project Euclid: 3 August 2009

Permanent link to this document
https://projecteuclid.org/euclid.lnms/1249305331

Digital Object Identifier
doi:10.1214/09-LNMS5713

Mathematical Reviews number (MathSciNet)
MR2681665

Zentralblatt MATH identifier
1271.62088

Subjects
Primary: 62J05: Linear regression
Secondary: 65C20: Models, numerical methods [See also 68U20]

Keywords
smoothing splines p-splines ridge regression varying coefficient models Bayesian prediction Kalman fiter adaptive selection

Rights
Copyright © 2009, Institute of Mathematical Statistics

Citation

Rojo, Javier. Mixed Models, Posterior Means and Penalized Least-Squares. Optimality, 216--236, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2009. doi:10.1214/09-LNMS5713. https://projecteuclid.org/euclid.lnms/1249305331


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