Bayesian Model Averaging with Exponentiated Least Square Loss
Download or Read eBook Bayesian Model Averaging with Exponentiated Least Square Loss PDF written by Dong Dai and published by . This book was released on 2013 with total page 116 pages. Available in PDF, EPUB and Kindle.
Author | : Dong Dai |
Publisher | : |
Total Pages | : 116 |
Release | : 2013 |
ISBN-10 | : OCLC:870291013 |
ISBN-13 | : |
Rating | : 4/5 (13 Downloads) |
Book Synopsis Bayesian Model Averaging with Exponentiated Least Square Loss by : Dong Dai
Book excerpt: Given a finite family of functions, the goal of model averaging is to construct a procedure that mimics the function from this family that is the closest to an unknown regression function. More precisely, we consider a general regression model with fi xed design and measure the distance between functions by mean squared error (MSE) at the design points. In this thesis, we propose a new method Bayesian model averaging with exponentiated least square loss (BMAX) to solve the model averaging problem optimally in a minimax sense.