Student Seminar Series - October 5, 2004
University of Minnesota
School of Statistics
College of Liberal Arts
Prediction
Intervals for Neural Networks: Literature Review and Future Directions
Sriram Thirumalai
Tuesday, October 5, 2004
1:00 PM, B60
Ford Hall
Minneapolis, East Bank Campus
Refreshments at 12:30 PM
300 Ford Hall
Abstract
Neural Networks are statistical models that model the response as
a nonlinear function of various linear combinations of the predictors.
The multi-layer non-linear functional form of neural networks that is
flexibly complex and yet tractable, its relatively distribution-free
approach and its ability to deal with large data sets makes it a
valuable alternative for statistical analysis in a host of
applications. A critical part of the use of neural networks in
statistical predictions is obtaining the confidence intervals about the
mean and future predictions. This project first reviews two broad
approaches from the extant literature to estimate prediction intervals
for neural networks. Next, building on the extant literature it
suggests alternate approaches to calculate the prediction intervals.