Kullback-Leibler aggregation and misspecified generalized linear models

Details Kullback-Leibler aggregation and misspecified generalized linear models Kullback-Leibler aggregation and misspecified generalized linear models

2009-11-16

arxiv.org/abs/0911.2919v4

Statistics

Machine Learning

To appear in the Annals of Statistics

Scientific paper

In a regression setup with deterministic design, we study the pure aggregation problem and introduce a natural extension from the Gaussian distribution to distributions in the exponential family. While this extension bears strong connections with generalized linear models, it does not require identifiability of the parameter or even that the model on the systematic component is true. It is shown that this problem can be solved by constrained and/or penalized likelihood maximization and we derive sharp oracle inequalities that hold both in expectation and with high probability. Finally all the bounds are proved to be optimal in a minimax sense.

Affiliated with

Also associated with

No associations

Rating

Kullback-Leibler aggregation and misspecified generalized linear models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Kullback-Leibler aggregation and misspecified generalized linear models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Kullback-Leibler aggregation and misspecified generalized linear models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-498311