Regression Analysis Under A Priori Parameter Restrictions: Springer Optimization and Its Applications, cartea 54
Autor Pavel S. Knopov, Arnold S. Korkhinen Limba Engleză Paperback – 24 oct 2013
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Specificații
ISBN-13: 9781461429555
ISBN-10: 1461429552
Pagini: 248
Ilustrații: XIV, 234 p.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.35 kg
Ediția:2012
Editura: Springer
Colecția Springer
Seria Springer Optimization and Its Applications
Locul publicării:New York, NY, United States
ISBN-10: 1461429552
Pagini: 248
Ilustrații: XIV, 234 p.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.35 kg
Ediția:2012
Editura: Springer
Colecția Springer
Seria Springer Optimization and Its Applications
Locul publicării:New York, NY, United States
Public țintă
GraduateTextul de pe ultima copertă
Construction of various models of objects under uncertainty is one of the most important problems in modern decision making theory. Regression models are some of the most prevalent tools for modeling under uncertainty and are widely applied in different branches of science such as in industrial research, agriculture, medicine, and business and economics. Regression Analysis Under A Priori Parameter Restrictions will be of interest to a broad spectrum of readers in applied mathematics, mathematical statistics, identification theory, systems analysis, econometrics, finance, optimization, and other scientific disciplines. Requiring a background in algebra, probability theory, mathematical statistics, and mathematical programming, this work may also be a useful supplement for advanced graduate courses in estimation theory, regression analysis, mathematical statistics, econometrics, mathematical programming and optimal control, and stochastic optimization.
The material contained in this monograph successfully combines interesting theoretical results with methods and algorithms for solving practical problems. It focuses on the construction of regression models with linear and non-linear constraint inequalities and is the first book in which the theoretical results lying in the background of construction and studying regression models with inequality constraints on parameters are presented systematically and solidly.
Problems are described and studied in a clear, precise, and rigorous method and include: calculation of estimates for regression parameters, determination of their asymptotic properties and accuracy of estimation, point and interval prediction by the regression, parameters of which are estimatedunder inequality constraints. The authors’ approach lends itself to numerous applications in various practical problems, several of which are discussed in detail.
The material contained in this monograph successfully combines interesting theoretical results with methods and algorithms for solving practical problems. It focuses on the construction of regression models with linear and non-linear constraint inequalities and is the first book in which the theoretical results lying in the background of construction and studying regression models with inequality constraints on parameters are presented systematically and solidly.
Problems are described and studied in a clear, precise, and rigorous method and include: calculation of estimates for regression parameters, determination of their asymptotic properties and accuracy of estimation, point and interval prediction by the regression, parameters of which are estimatedunder inequality constraints. The authors’ approach lends itself to numerous applications in various practical problems, several of which are discussed in detail.
Caracteristici
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