Titlebook: Statistical Analysis for High-Dimensional Data; The Abel Symposium 2 Arnoldo Frigessi,Peter Bühlmann,Marina Vannucci Conference proceedings

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Bayesian Penalty Mixing: The Case of a Non-separable Penalty,of estimating sparse high-dimensional normal means. Separable penalized likelihood estimators are known to have a Bayesian interpretation as posterior modes under independent product priors. Such estimators can achieve rate-minimax performance when the correct level of sparsity is known. A fully Bay

BRIDE

Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data,ar regression might, for example, be markedly different for distinct groups of the data. Maximin effects have been proposed as a computationally attractive way to estimate effects that are common across all data without fitting a mixture distribution explicitly. So far just point estimators of the c

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Incisor

Some Themes in High-Dimensional Statistics,and integration of multiple high-dimensional data, but this categorization is not exhaustive. The contributions by some of the participants, appearing as chapters in the book, include both in-depth reviews and development of new statistical methodology, applications and theory.

FICE

Instantaneous

FID

Duodenitis

iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH Data,sociations. In this chapter we review the model and present the functions provided in ., an R package based on a C implementation of the inferential algorithm. Lastly, we illustrate the method via a case study on ovarian cancer.

ETCH

An Imputation Method for Estimating the Learning Curve in Classification Problems,fold increase in the size of the training set relative to the available data, and that the proposed imputation approach outperforms an alternative estimation approach based on parameterizing the learning curve. We illustrate the method with an application that predicts the risk of disease progression for people with chronic lymphocytic leukemia.

cardiovascular

Laplace Approximation in High-Dimensional Bayesian Regression,nsional scenario in which . and ., and thus also the number of considered models, may increase with .. Moreover, we show how this connection between marginal likelihood and Laplace approximation can be used to obtain consistency results for Bayesian approaches to variable selection in high-dimensional regression.