Optimization 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.
Optimization
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Author : Kenneth Lange
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-06-17
Optimization written by Kenneth Lange 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 2004-06-17 with Business & Economics categories.
Lange is a Springer author of other successful books. This is the first book that emphasizes the applications of optimization to statistics. The emphasis on statistical applications will be especially appealing to graduate students of statistics and biostatistics.
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
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.
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.
Optimization Based Data Mining Theory And Applications
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Author : Yong Shi
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-16
Optimization Based Data Mining Theory And Applications written by Yong Shi 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 2011-05-16 with Computers categories.
Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
Statistical Analysis And Optimization For Vlsi Timing And Power
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Author : Ashish Srivastava
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-04
Statistical Analysis And Optimization For Vlsi Timing And Power written by Ashish Srivastava 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 2006-04-04 with Technology & Engineering categories.
Statistical Analysis and Optimization For VLSI: Timing and Power is a state-of-the-art book on the newly emerging field of statistical computer-aided design (CAD) tools. The very latest research in statistical timing and power analysis techniques is included, along with efforts to incorporate parametric yield as the key objective function during the design process. Included is the necessary mathematical background on techniques which find widespread use in current analysis and optimization. The emphasis is on algorithms, modeling approaches for process variability, and statistical techniques that are the cornerstone of the probabilistic CAD movement. The authors also describe recent optimization approaches to timing yield and contrast them to deterministic optimization. The work will enable new researchers in this area to come up to speed quickly, as well as provide a handy reference for those already working in CAD tool development.
Open Problems In Optimization And Data Analysis
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Author : Panos M. Pardalos
language : en
Publisher: Springer
Release Date : 2018-12-04
Open Problems In Optimization And Data Analysis written by Panos M. Pardalos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-04 with Mathematics categories.
Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book. Each contribution provides the fundamentals needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline. The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016.
Mm Optimization Algorithms
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Author : Kenneth Lange
language : en
Publisher: SIAM
Release Date : 2016-07-11
Mm Optimization Algorithms written by Kenneth Lange and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-11 with Mathematics categories.
MM Optimization Algorithms offers an overview of the MM principle, a device for deriving optimization algorithms satisfying the ascent or descent property. These algorithms can separate the variables of a problem, avoid large matrix inversions, linearize a problem, restore symmetry, deal with equality and inequality constraints gracefully, and turn a nondifferentiable problem into a smooth problem. The author presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics; derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining; and summarizes a large amount of literature that has not reached book form before.