Optimization Methods In Statistics

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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 Social 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 : 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 Methods In Statistics

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Author : Adele Cutler
language : en
Publisher:
Release Date : 1992

Optimization Methods In Statistics written by Adele Cutler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.

Optimization Techniques In Statistics

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Author : Jagdish S. Rustagi
language : en
Publisher: Academic Press
Release Date : 1994-04-25

Optimization Techniques 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 1994-04-25 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 estimatesMarkov decision processesProgramming methods used to optimize monitoring of patients in hospitalsDerivation of the Neyman-Pearson lemmaThe search for optimal designsSimulation of a steel millSuitable 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.

Computer Oriented Statistical And Optimization Methods

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Author :
language : en
Publisher: Krishna Prakashan Media
Release Date :

Computer Oriented Statistical And Optimization Methods written by and has been published by Krishna Prakashan Media this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Optimization Methods In Statistics

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Author : J. S. Rustagi
language : en
Publisher:
Release Date : 1971

Optimization Methods In Statistics written by J. 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.

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 Business & Economics categories.

Optimizing Methods in Statistics.

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.

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.

PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries.

Model Optimization Methods For Efficient And Edge Ai

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Author : Pethuru Raj Chelliah
language : en
Publisher: John Wiley & Sons
Release Date : 2024-11-13

Model Optimization Methods For Efficient And Edge Ai written by Pethuru Raj Chelliah and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-13 with Computers categories.

Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.