Front Cover; Optimizing Methods in Statistics; Copyright Page; Table of Contents; CONTRIBUTORS; PREFACE; CHAPTER 1. THE EFFICIENT ESTIMATION OF A PARAMETER MEASURABLE BY TWO INSTRUMENTS OF UNKNOWN PRECISIONS; 1. Introduction and Summary; 2. Statement of Problem; 3. Related Problems; 4. The Method; 5. Motivation for the Method; 6. Bandit Problems and Their Applicability; 7. Simulations; 8. Comments; References; CHAPTER 2.OPTIMIZATION PROBLEMS IN SIMULATION; References; CHAPTER 3.SOME OPTIMIZATION PROBLEMS IN PARAMETER ESTIMATION; 0. Summary; 1. Maximum Likelihood Estimation

2. Linear EstimationReferences; CHAPTER 4.OPTIMAL DESIGNS AND SPLINE REGRESSION; 1. Introduction; 2. Spline Functions; 3. Optimal Designs; 4. Comparison of D and I Optimal Designs; 5. Examples; 6. Computational Procedures; References; CHAPTER 5.ISOTONIC APPROXIMATION; 1. Introduction; 2. Isotonic Estimation; 3. Duality Theory; 4. Application to Failure Rate Estimation; References; CHAPTER 6. ASYMPTOTICALLY EFFICIENT ESTIMATION OF NONPARAMETRIC REGRESSION COEFFICIENTS; Abstract; CHAPTER 7. COMPARISONS OF ORDER STATISTICS AND OF SPACINGS FROM HETEROGENEOUS DISTRIBUTIONS; Abstract

1. Introduction2. Comparisons for k-out-of-n Systems and Corresponding Order Statistic Implications; 3. Spacings; 4. Applications; References; CHAPTER 8. MOMENT PROBLEMS WITH CONVEXITY CONDITIONS ; 1. Introduction; 2. Statement of the moment problem; 3. Transforming a moment problem; 4. Auxiliary results; 5. A class of generalized convex distributions; 6. Some concrete moment problems with convexity conditions; 7. Inequalities of the Camp-Meidell type; References; CHAPTER 9.VARIATIONAL METHODS IN ADAPTIVE FILTERING; Abstract; I. Introduction; II. Models for Adaptive Filtering

III. Maximum A Posterior Cost FunctionFor Adaptive FilteringIV. Cost Functions for MaximumLikelihood Identification; V. Invariant Imbedding; VI. Sequential Nonlinear Filtering andPlant Parameter Identification; VII. Adaptive Estimation of Prior Statistics; VIII. Summary; IX. Acknowledgments; X. References; CHAPTER 10. NON LINEAR FILTERING; 1. Introduction; 2. A General Approach to Non-LinearFiltering Problems; 3. Discussion of General Results; 4. An Extension of Kaiman and Bucyfs Results; References

CHAPTER 11. A CONVERGENCE THEOREM FOR NON NEGATIVE ALMOST SUPERMARTINGALES AND SOME APPLICATIONS1. Introduction and Summary; 2. Proof of Theorem 1, Theorem 2,and Inequalities; 3. The Strong Law of Large Numbers; 4. Stochastic Approximation; 5. Minimax Theorem for Vector Payoffs; 7. Hâjek-Renyi-Chow Inequality; References; CHAPTER 12. ON RELATIONSHIPS BETWEEN THE NEYMAN-PEARSON PROBLEM AND LINEAR PROGRAMMING; Introduction; Relationships Between the Problems D-0 and D-1; Concluding Remarks; Acknowledgments; References; CHAPTER 13.STATISTICAL CONTROL OF OPTIMIZATION; 1. Introduction