COVID-19 Update: We are currently shipping orders daily. However, due to transit disruptions in some geographies, deliveries may be delayed. To provide all customers with timely access to content, we are offering 50% off Science and Technology Print & eBook bundle options. Terms & conditions.
Optimizing Methods in Statistics - 1st Edition - ISBN: 9780126045505, 9781483260341

Optimizing Methods in Statistics

1st Edition

Proceedings of a Symposium Held at the Center for Tomorrow, the Ohio State University, June 14-16, 1971

Editor: Jagdish S. Rustagi
eBook ISBN: 9781483260341
Imprint: Academic Press
Published Date: 1st January 1971
Page Count: 504
Sales tax will be calculated at check-out Price includes VAT/GST
Price includes VAT/GST

Institutional Subscription

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.


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.

Table of Contents



The Efficient Estimation of a Parameter Measurable by Two Instruments of Unknown Precisions

Optimization Problems in Simulation

Some Optimization Problems in Parameter Estimation

Optimal Designs and Spline Regressions

Isotonic Approximation

Asymptotically Efficient Estimation of Nonparametric Regression Coefficients (Abstract)

Comparisons of Order Statistics and of Spacings from Heterogeneous Distributions

Moment Problems with Convexity Conditions I

Variational Methods in Adaptive Filtering

Non Linear Filtering

A Convergence Theorem for Non Negative Almost Supermartingales and Some Applications

On Relationships Between the Neyman-Pearson Problem and Linear Programming

Statistical Control of Optimization

Current Capabilities in Mathematical Programming (Abstract)

Patterns and Search Statistics

Necessary Conditions for a Local Optimum without Prior Constraint Qualification

Mathematical Models for Statistical Decision Theory

Chance-Constrained Programming: An Extension of Statistical Method

Stochastic Allocation of Spare Components

Outlier Proneness of Phenomena and of Related Distributions

Problem Areas Requiring Optimizing Methods

Stochastic Approximation

Allocation of Observations in Ranking and Selection with Unequal Variances (Abstract)

Sequences of Minimal Fractions of 2n Designs of Resolution V (Abstract)

Optimum Interval Estimation for the Largest Scale Parameter

c-Sample Tests of Homogeneity Against Ordered Alternatives (Abstract)



No. of pages:
© Academic Press 1971
1st January 1971
Academic Press
eBook ISBN:

About the Editor

Jagdish S. Rustagi

Ratings and Reviews