Owing to the scale and complexity of data sets at this time remaining gathered in parts such as well being, transportation, environmental science, engineering, data technology, organization and finance, modern day quantitative analysts are seeking enhanced and proper computational and statistical procedures to investigate, product and draw inferences from large data. This book aims to introduce appropriate…
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Author : Shen Liu
Genre : Mathematics
Summary : Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. Advanced computational and statistical methodologies for analysing big data are developed Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable Case studies are discussed to demonstrate the implementation of the developed methods Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation Computing code/programs are provided where appropriate
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Author : James E. Gentle
Genre : Mathematics
Summary : Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods.
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Author : Ingvar Eidhammer
Genre : Mathematics
Summary : The definitive introduction to data analysis in quantitativeproteomics This book provides all the necessary knowledge about massspectrometry based proteomics methods and computational andstatistical approaches to pursue the planning, design and analysisof quantitative proteomics experiments. The author’scarefully constructed approach allows readers to easily make thetransition into the field of quantitative proteomics. Throughdetailed descriptions of wet-lab methods, computational approachesand statistical tools, this book covers the full scope of aquantitative experiment, allowing readers to acquire new knowledgeas well as acting as a useful reference work for more advancedreaders. Computational and Statistical Methods for ProteinQuantification by Mass Spectrometry: Introduces the use of mass spectrometry in proteinquantification and how the bioinformatics challenges in this fieldcan be solved using statistical methods and various softwareprograms. Is illustrated by a large number of figures and examples aswell as numerous exercises. Provides both clear and rigorous descriptions of methods andapproaches. Is thoroughly indexed and cross-referenced, combining thestrengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches andstatistical and computational methods. With clear and thorough descriptions of the various methods andapproaches, this book is accessible to biologists, informaticians,and statisticians alike and is aimed at readers across the academicspectrum, from advanced undergraduate students to post doctoratesentering the field.
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Author :
Genre : Mathematics
Summary : Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods.
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Author : John M. Chambers
Genre : Mathematical statistics
Summary : Programming; Data management and manipulation; Numerical computations; Linear models; Nonlinear models; Simulation of Random processes; Computational graphics.
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Author : Peter J. Huber
Genre : Mathematics
Summary : This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.
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Author : Nadja Bauer
Genre : Computers
Summary : This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.
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Author : Siegmund Brandt
Genre :
Summary :
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Author : Kenneth Lange
Genre : Business & Economics
Summary : Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.
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Author : James E. Gentle
Genre : Computers
Summary : Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books