Optimizing Methods In Statistics
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Optimizing Methods In Statistics
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Author : Jagdish S. Rustagi
language : en
Publisher: Academic Press
Release Date : 2014-05-10
Optimizing Methods In Statistics written by Jagdish S. Rustagi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-10 with Mathematics categories.
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.
Optimization Techniques In Statistics
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Author : Jagdish S. Rustagi
language : en
Publisher: Elsevier
Release Date : 2014-05-19
Optimization Techniques In Statistics written by Jagdish S. Rustagi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Mathematics categories.
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 numerical techniques using gradients or direct search, to linear, nonlinear, and dynamic programming using the Kuhn-Tucker conditions or the Pontryagin maximal principle. Variational methods and optimization in function spaces are also discussed, as are stochastic optimization in simulation, including annealing methods. The text features numerous applications, including: Finding maximum likelihood estimates, Markov decision processes, Programming methods used to optimize monitoring of patients in hospitals, Derivation of the Neyman-Pearson lemma, The search for optimal designs, Simulation of a steel mill. Suitable as both a reference and a text, this book will be of interest to advanced undergraduate or beginning graduate students in statistics, operations research, management and engineering sciences, and related fields. Most of the material can be covered in one semester by students with a basic background in probability and statistics. Covers optimization from traditional methods to recent developments such as Karmarkars algorithm and simulated annealing Develops a wide range of statistical techniques in the unified context of optimization Discusses applications such as optimizing monitoring of patients and simulating steel mill operations Treats numerical methods and applications Includes exercises and references for each chapter Covers topics such as linear, nonlinear, and dynamic programming, variational methods, and stochastic optimization
Introduction To Optimization Methods And Their Application In Statistics
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Author : B. Everitt
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Introduction To Optimization Methods And Their Application In Statistics written by B. Everitt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
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 methods and their use in several important areas of statistics. It does not pretend to provide either a complete treatment of optimization techniques or a comprehensive review of their application in statistics; such a review would, of course, require a volume several orders of magnitude larger than this since almost every issue of every statistics journal contains one or other paper which involves the application of an optimization method. It is hoped that the text will be useful to students on applied statistics courses and to researchers needing to use optimization techniques in a statistical context. Lastly, my thanks are due to Bertha Lakey for typing the manuscript.
Optimizing Methods In Statistics
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Author :
language : en
Publisher:
Release Date : 1971
Optimizing Methods In Statistics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with categories.
Optimization Methods For Applications In Statistics
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Author : James E. Gentle
language : en
Publisher:
Release Date : 2006-01
Optimization Methods For Applications In Statistics written by James E. Gentle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01 with Mathematical optimization categories.
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Optimization Techniques In Statistics
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Author : Rustagi
language : en
Publisher:
Release Date : 1992-06-01
Optimization Techniques In Statistics written by Rustagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-06-01 with categories.
Optimizing Methods In Statistics
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Author : Jagdish S. Rustagi
language : en
Publisher:
Release Date : 1979
Optimizing Methods In Statistics written by Jagdish S. Rustagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1979 with Mathematics categories.
Optimizing Methods In Statistics
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Author : Jagdish S. Rustagi
language : en
Publisher:
Release Date : 1971
Optimizing Methods In Statistics written by Jagdish S. Rustagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with categories.
Process Optimization
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Author : Enrique del Castillo
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-09-14
Process Optimization written by Enrique del Castillo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-14 with Mathematics categories.
This book covers several bases at once. It is useful as a textbook for a second course in experimental optimization techniques for industrial production processes. In addition, it is a superb reference volume for use by professors and graduate students in Industrial Engineering and Statistics departments. It will also be of huge interest to applied statisticians, process engineers, and quality engineers working in the electronics and biotech manufacturing industries. In all, it provides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, stopping rules in experimental optimization, and more.
Statistical Inference Via Convex Optimization
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Author : Anatoli Juditsky
language : en
Publisher: Princeton University Press
Release Date : 2020-04-07
Statistical Inference Via Convex Optimization written by Anatoli Juditsky and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-07 with Mathematics categories.
This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems—sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals—demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems. Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.