Statistical learning and selective inference

statistical inference via empirical bayes approach for stationary and dynamic contingency tables

چکیده ندارد.

On statistical inference for selective genotyping

Selective Inference and Learning Mixed Graphical Models

This thesis studies two problems in modern statistics. First, we study selective inference, or inference for hypothesis that are chosen after looking at the data. The motiving application is inference for regression coefficients selected by the lasso. We present the Condition-onSelection method that allows for valid selective inference, and study its application to the lasso, and several other ...

Procrustean statistical inference of deformations

A two step method has been devised for the statistical inference of deformation changes. In the first step of this method and based on Procrustes analysis of deformation tensors, the significance of the change in a time or space series of deformation tensors is statistically analyzed. In the second step significant change(s) in deformations are localized. In other words, they are assigned to ce...

Statistical inference: learning in artificial neural networks.

Artificial neural networks (ANNs) are widely used to model low-level neural activities and high-level cognitive functions. In this article, we review the applications of statistical inference for learning in ANNs. Statistical inference provides an objective way to derive learning algorithms both for training and for evaluation of the performance of trained ANNs. Solutions to the over-fitting pr...