This monograph focuses on the construction of regression models with linear and non-linear constrain inequalities from the theoretical point of view. In includes analysis of the properties of regression with inequality constrains.
This monograph focuses on the construction of regression models with linear and non-linear constrain inequalities from the theoretical point of view. Unlike previous publications, this volume analyses the properties of regression with inequality constrains, investigating the flexibility of inequality constrains and their ability to adapt in the presence of additional a priori information
The implementation of inequality constrains improves the accuracy of models, and decreases the likelihood of errors. Based on the obtained theoretical results, a computational technique for estimation and prognostication problems is suggested. This approach lends itself to numerous applications in various practical problems, several of which are discussed in detail
The book is useful resource for graduate students, PhD students, as well as for researchers who specialize in applied statistics and optimization. This book may also be useful to specialists in other branches of applied mathematics, technology, econometrics and finance
Presents four central topics in stochastic optimization: calculation of parameter estimates, the asymptotic theory of estimates, estimation theory for small samples, and prediction theory
Describes applications to practical problems from various areas of study including econometrics, clinical medicine, and several other experimental sciences
Considers the problems of prediction by means of linear regression in the context of small samples