3 edition of Optimization in statistics found in the catalog.
by North-Holland Pub. Co., Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co. in Amsterdam, New York, New York, NY
Written in English
|Statement||edited by S.H. Zanakis, J.S. Rustagi.|
|Series||TIMS studies in the management sciences ;, v. 19|
|Contributions||Zanakis, S. H., Rustagi, Jagdish S.|
|LC Classifications||QA276 .O67 1982|
|The Physical Object|
|Pagination||x, 333 p. :|
|Number of Pages||333|
|LC Control Number||83117748|
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The mathematical techniques of optimization are fundamentalto statistical theory and practice. In this book, Jagdish Rustagi provides full-spectrum coverage of these methods, ranging from classical Cited by: Optimizing Method in Statistics is a compendium of papers dealing with variational methods, regression analysis, mathematical programming, optimum seeking methods, stochastic control, optimum design of experiments, optimum spacings, and order statistics.
Table of contents 1 - Synopsis. Pages 1 - 10 The modern theory of statistics makes extensive use of optimization techniques for 2 - Classical Optimization Techniques. Pages 11 - 40 The classical techniques of optimization include methods of maxima 3 - Optimization.
Statistics help guide us to optimal decisions under uncertainty. A large variety of statistical problems are essentially solutions to optimization problems.
The mathematical techniques of optimization are fundamentalto statistical Book Edition: 1. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical.
Statistics help guide us to optimal decisions under uncertainty. A large variety of statistical problems are essentially solutions to optimization problems. The mathematical techniques of optimization are fundamentalto statistical theory and practice.
In this book, Jagdish Rustagi provides full-spectrum coverage of these methods, ranging from classical optimization. A2A. I hope you mean Convex Optimization. Single handedly this book is the most popular and celebrated one .
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Optimization Methods for Computational Statistics and Data Analysis Stephen Wright University of Wisconsin-Madison SAMSI Optimization Opening Workshop, August Wright (UW-Madison) Optimization.
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PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization.
The book Brand: Springer US. This book has been used in an upper division undergraduate course about optimization given in the Mathematics Department at Northwestern University.
Only deterministic problems with a continuous choice of options are considered, hence optimization File Size: KB. Optimization with multi objective functions and global optimization have also found use in solving statistical problems arising in various fields.
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Active 1 year, 4 months ago. Here are some of the criteria (subjective) for a good optimization book. Introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book.
Financial Risk Modelling and Portfolio Optimization Cited by: Applications of quadratic programming in statistics, B.M. Brown and C.R.
Goodall Bounds on inconsistent inferences for sequences of trials with varying probabilities, H. Chernoff and Y. Wu PART VI OPTIMIZATION OF NETWORK FLOW Optimization. Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy is the first book to present a comprehensive introduction to MOST.
It will be an essential. Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike a .This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art .Optimization techniques in statistics.
Boston: Academic Press, © (OCoLC) Material Type: Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: Jagdish .