Optimizing methods in statistics proceedings of a symposium, held at the Center for Tomorrow, The Ohio State University .

Editor Jagdish S. Rustagi

ISBN 10: 012604550X / ISBN 13: 9780126045505

Published by New York, Academic Press, 1971

About this Item

Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 62 OPT 9780126045505 Sprache: Englisch Gewicht in Gramm: 1150.

Seller Inventory # 2503831

Bibliographic Details

• Title: Optimizing methods in statistics proceedings of a symposium, held at the Center for Tomorrow, The Ohio State University .
• Publisher: New York, Academic Press
• Publication Date: 1971
• Language: English
• ISBN 10: 012604550X
• ISBN 13: 9780126045505
• Binding: Hardcover
• Seller catalogs:
  • SA MATHEMATIK

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.