Statistical Models for Control and Optimization Techniques.
Abstract
The main thrust of the research has been to continue the development of univariate and multivariate time series and dynamic model-building techniques. Important problems are associated with estimation of parameters which appear non-linearly. This has been tackled by use of Bayes' methods. Investigations have been made into lagged variable forecasting techniques, behavior of sample autocorrelation functions for non-stationary series, distribution theory of partial autocorrelation functions, new methods for estimation of parameters in non-linear models. The use of Reproducing Kernel Hilbert spaces as a tool to solve optimization problems occurring in continuous time control is being studied extensively. Numerical methods for solving linear operator equations occurring in control problems were developed and established. A numerical method for minimizing a quadratic functional subject to a continuous family of linear inequality constraints was analyzed. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1972
- Accession Number
- AD0746694
Entities
People
- George E. P. Box
- Grace Wahba
- Irwin Guttman
Organizations
- University of Wisconsin–Madison