Optimization techniques in statistics
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Optimization techniques in statistics

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Published by Academic Press in Boston .
Written in English

Subjects:

  • Mathematical optimization.,
  • Mathematical statistics.,
  • Programming (Mathematics)

Book details:

Edition Notes

Includes bibliographical references (p. 325-341) and indexes.

StatementJagdish S. Rustagi.
SeriesStatistical modeling and decision science
Classifications
LC ClassificationsQA402.5 .R877 1994
The Physical Object
Paginationxii, 359 p. :
Number of Pages359
ID Numbers
Open LibraryOL1078728M
ISBN 100126045550
LC Control Number94002016

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May 19,  · 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 and Lagrange multipliers, to numerical techniques using gradients or direct search, to linear, nonlinear, and dynamic programming using Book Edition: 1. 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 and Lagrange multipliers, to numerical techniques using gradients or direct search, to linear, nonlinear, and dynamic programming using Cited by: Optimization Techniques in Statistics (Statistical Modeling and Decision Science) - Kindle edition by Jagdish S. Rustagi. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Optimization Techniques in Statistics (Statistical Modeling and Decision Science).Manufacturer: Academic Press. Optimization techniques are used to find the values of a set of parameters which maximize or minimize some objective function of interest. Such methods have become of great importance in statistics for estimation, model fitting, etc. This text attempts to give a brief introduction to optimization.

Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations Author: Xin-She Yang. Covers methods of optimization, fundamental to statistical theory and practice, such as classical optimization and Lagrange multipliers, techniques using gradients or . 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 and Lagrange multipliers, to. The optimization methods depend on the computer and the programming methods available. When it is necessary to resort to simulation, large numbers of random variables are used, typically in rather complicated ways. As this uses a great deal of computer time, optimization is clearly called for.

The book titled is based on optimization techniques and O.R. related courses for undergraduate and postgraduate engineering and mathematics students of various universities as well as for. (This is a live list. Edits and additions welcome) Lecture notes: Highly recommended: video lectures by Prof. S. Boyd at Stanford, this is a rare case where watching live lectures is better than reading a book. * EE Introduction to Linear D. Optimization Methods for Computational Statistics and Data Analysis Stephen Wright University of Wisconsin-Madison SAMSI Optimization Opening Workshop, August Wright (UW-Madison) Optimization in Data Analysis August 1 / Get this from a library! Optimization techniques in statistics. [Jagdish S Rustagi] -- 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.