Search arXivSearch

arXiv · math/0503495

On the shrinkage behavior of partial least squares regression

Abstract

We present a formula for the shrinkage factors of the Partial Least Squares regression estimator and deduce some of their properties, in particular the known fact that some of the factors are >1. We investigate the effect of shrinkage factors for the Mean Squared error of linear estimators and illustrate that we cannot extend the results to nonlinear estimators. In particular, shrinkage factors >1 do not automatically lead to a poorer Mean Squared Error. We investigate empirically the effect of bounding the the absolute value of the Partial Least Squares shrinkage factors by 1.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nicole Kraemer. 2005-03-23. On the shrinkage behavior of partial least squares regression. https://arxiv.org/abs/math/0503495

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A note on the distribution of the partial correlation coefficient with nonparametrically estimated marginal regressions

There has been much interest in the nonparametric testing of conditional independence in the econometric and statistical literature, but the simplest and potentially most useful method, based on the sample partial correlation, seems to have been overlooked, its distribution only having been investigated in some simple parametric instances. The present note shows that an easy to apply permutation test based on the sample partial correlation with nonparametrically estimated marginal regressions has good large and small sample properties.

math.ST

Instance-Log-Optimality of Portfolio-Based E-Processes and their Sequential Hypothesis Tests

We consider the problem of sequential hypothesis testing using $e$-processes. For a rich class of composite testing problems---which include bounded mean testing, equal mean testing for bounded random tuples, and some key ingredients of two-sample and independence testing as special cases---we show that any $e$-process satisfying a certain sublinear regret bound is asymptotically and almost surely instance-log-optimal for a composite alternative. This is a strong notion of optimality that has not previously been established for the aforementioned problems, and we provide explicit test supermartingales and $e$-processes satisfying this notion in a more general case. Furthermore, we derive matching lower and upper bounds on the expected rejection time in the high-confidence regime for the resulting sequential tests in all of these cases. The proofs of these results make weak, algorithm-agnostic moment assumptions and rely on a proof technique involving the aforementioned regret and a family of numeraire portfolios. Finally, we discuss how all of these theorems hold in a distribution-uniform sense, a notion of log-optimality that is stronger still and seems to be new to the literature.

math.ST

Common Drivers in Sparsely Interacting Hawkes Processes

We study a multivariate Hawkes process as a model for time-continuous relational event networks. The model does not assume the network to be known, it includes covariates, and it allows for both common drivers, parameters common to all the actors in the network, and also local parameters specific for each actor. We derive rates of convergence for all of the model parameters when both the number of actors and the time horizon tends to infinity. To prevent an exploding network, sparseness is assumed. We also discuss numerical aspects.

math.ST