Optimizing Methods in Statistics: Proceedings of a Symposium held at the Center for Tomorrow, The Ohio State University, June 14-16, 1971

Jagdish S. Rustagi, editor

ISBN 10: 012604550X / ISBN 13: 9780126045505

Published by Academic Press, 1971

About this Item

488 pp., hardcover, very good in a worn dust jacket. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

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Bibliographic Details

• Title: Optimizing Methods in Statistics: Proceedings of a Symposium held at the Center for Tomorrow, The Ohio State University, June 14-16, 1971
• Publisher: Academic Press
• Publication Date: 1971
• Language: English
• ISBN 10: 012604550X
• ISBN 13: 9780126045505
• Binding: Hardcover
• Condition: Very Good

About this title

Synopsis

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. One paper reviews three optimization problems encountered in parameter estimation, namely, 1) iterative procedures for maximum likelihood estimation, based on complete or censored samples, of the parameters of various populations; 2) optimum spacings of quantiles for linear estimation; and 3) optimum choice of order statistics for linear estimation. Another paper notes the possibility of posing various adaptive filter algorithms to make the filter learn the system model while the system is operating in real time. By reducing the time necessary for process modeling, the time required to implement the acceptable system design can also be reduced One paper evaluates the parallel structure between duality relationships for the linear functional version of the generalized Neyman-Pearson problem, as well as the duality relationships of linear programming as these apply to bounded-variable linear programming problems. The compendium can prove beneficial to mathematicians, students, and professor of calculus, statistics, or advanced mathematics.