Stepwise Signal Extraction via Marginal Likelihood

Stepwise Signal Extraction via Marginal Likelihood.

This paper studies the estimation of stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal li...

Marginal likelihood estimation via power posteriors

Model choice plays an increasingly important role in Statistics. From a Bayesian perspective a crucial goal is to compute the marginal likelihood of the data for a given model. This however is typically a difficult task since it amounts to integrating over all model parameters. The aim of this paper is to illustrate how this may be achieved using ideas from thermodynamic integration or path sam...

Variable Selection for Linear Transformation Models via Penalized Marginal Likelihood

We study the problem of variable selection for linear transformation models, a class of general semiparametric models for censored survival data. The penalized marginal likelihood methods with shrinkage-type penalties are proposed to automate variable selection in linear transformation models; we consider the LASSO penalty and propose a new penalty called the adaptive-LASSO (ALASSO). Unlike the...

Marc Wildi Real - Time Signal Extraction Beyond Maximum Likelihood Principles

Marc Wildi Real - Time Signal Extraction Beyond Maximum Likelihood