Functional Data Analysis A Complete Guide - 2020 Edition
Functional Data Analysis A Complete Guide - 2020 Edition

Author: Gerardus Blokdyk

Publisher: 5starcooks

Published: 2020-05-22

Total Pages: 306

ISBN-13: 9781867409908

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What details are required of the Functional data analysis cost structure? How do you assess the Functional data analysis pitfalls that are inherent in implementing it? What is the scope of Functional data analysis? Is pre-qualification of suppliers carried out? Where can you break convention? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Functional Data Analysis investments work better. This Functional Data Analysis All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Functional Data Analysis Self-Assessment. Featuring 947 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Functional Data Analysis improvements can be made. In using the questions you will be better able to: - diagnose Functional Data Analysis projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Functional Data Analysis and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Functional Data Analysis Scorecard, you will develop a clear picture of which Functional Data Analysis areas need attention. Your purchase includes access details to the Functional Data Analysis self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Functional Data Analysis Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Functional Analysis A Complete Guide - 2020 Edition
Functional Analysis A Complete Guide - 2020 Edition

Author: Gerardus Blokdyk

Publisher: 5starcooks

Published: 2020-02-16

Total Pages: 304

ISBN-13: 9781867332442

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In a project to restructure Functional analysis outcomes, which stakeholders would you involve? What does Functional analysis success mean to the stakeholders? Is the Functional analysis test/monitoring cost justified? What are your current levels and trends in key measures or indicators of Functional analysis product and process performance that are important to and directly serve your customers? How do these results compare with the performance of your competitors and other organizations with similar offerings? How can you improve Functional analysis? This premium Functional Analysis self-assessment will make you the accepted Functional Analysis domain auditor by revealing just what you need to know to be fluent and ready for any Functional Analysis challenge. How do I reduce the effort in the Functional Analysis work to be done to get problems solved? How can I ensure that plans of action include every Functional Analysis task and that every Functional Analysis outcome is in place? How will I save time investigating strategic and tactical options and ensuring Functional Analysis costs are low? How can I deliver tailored Functional Analysis advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Functional Analysis essentials are covered, from every angle: the Functional Analysis self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Functional Analysis outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Functional Analysis practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Functional Analysis are maximized with professional results. Your purchase includes access details to the Functional Analysis self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Functional Analysis Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Introduction to Functional Data Analysis
Introduction to Functional Data Analysis

Author: Piotr Kokoszka

Publisher: CRC Press

Published: 2017-09-27

Total Pages: 307

ISBN-13: 1498746624

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Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems. The material of the book can be roughly divided into four parts of approximately equal length: 1) basic concepts and techniques of FDA, 2) functional regression models, 3) sparse and dependent functional data, and 4) introduction to the Hilbert space framework of FDA. The book assumes advanced undergraduate background in calculus, linear algebra, distributional probability theory, foundations of statistical inference, and some familiarity with R programming. Other required statistics background is provided in scalar settings before the related functional concepts are developed. Most chapters end with references to more advanced research for those who wish to gain a more in-depth understanding of a specific topic.

Inference for Functional Data with Applications
Inference for Functional Data with Applications

Author: Lajos Horváth

Publisher: Springer Science & Business Media

Published: 2012-05-08

Total Pages: 426

ISBN-13: 1461436559

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This book presents recently developed statistical methods and theory required for the application of the tools of functional data analysis to problems arising in geosciences, finance, economics and biology. It is concerned with inference based on second order statistics, especially those related to the functional principal component analysis. While it covers inference for independent and identically distributed functional data, its distinguishing feature is an in depth coverage of dependent functional data structures, including functional time series and spatially indexed functions. Specific inferential problems studied include two sample inference, change point analysis, tests for dependence in data and model residuals and functional prediction. All procedures are described algorithmically, illustrated on simulated and real data sets, and supported by a complete asymptotic theory. The book can be read at two levels. Readers interested primarily in methodology will find detailed descriptions of the methods and examples of their application. Researchers interested also in mathematical foundations will find carefully developed theory. The organization of the chapters makes it easy for the reader to choose an appropriate focus. The book introduces the requisite, and frequently used, Hilbert space formalism in a systematic manner. This will be useful to graduate or advanced undergraduate students seeking a self-contained introduction to the subject. Advanced researchers will find novel asymptotic arguments.

Functional Data Analysis with R and MATLAB
Functional Data Analysis with R and MATLAB

Author: James Ramsay

Publisher: Springer Science & Business Media

Published: 2009-06-29

Total Pages: 213

ISBN-13: 0387981853

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The book provides an application-oriented overview of functional analysis, with extended and accessible presentations of key concepts such as spline basis functions, data smoothing, curve registration, functional linear models and dynamic systems Functional data analysis is put to work in a wide a range of applications, so that new problems are likely to find close analogues in this book The code in R and Matlab in the book has been designed to permit easy modification to adapt to new data structures and research problems

Applied Functional Data Analysis
Applied Functional Data Analysis

Author: J.O. Ramsay

Publisher: Springer

Published: 2007-11-23

Total Pages: 194

ISBN-13: 0387224653

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This book contains the ideas of functional data analysis by a number of case studies. The case studies are accessible to research workers in a wide range of disciplines. Every reader should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought. There is an associated web site with MATLABr and S?PLUSr implementations of the methods discussed.

Functional and Shape Data Analysis
Functional and Shape Data Analysis

Author: Anuj Srivastava

Publisher: Springer

Published: 2016-10-03

Total Pages: 454

ISBN-13: 1493940201

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This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered—from introductory theory to algorithmic implementations and some statistical case studies—is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses. These tools, gleaned from geometry, algebra, statistics, and computational science, are traditionally scattered across different courses, departments, and disciplines; Functional and Shape Data Analysis offers a unified, comprehensive solution by integrating the registration problem into shape analysis, better preparing graduate students for handling future scientific challenges. Recently, a data-driven and application-oriented focus on shape analysis has been trending. This text offers a self-contained treatment of this new generation of methods in shape analysis of curves. Its main focus is shape analysis of functions and curves—in one, two, and higher dimensions—both closed and open. It develops elegant Riemannian frameworks that provide both quantification of shape differences and registration of curves at the same time. Additionally, these methods are used for statistically summarizing given curve data, performing dimension reduction, and modeling observed variability. It is recommended that the reader have a background in calculus, linear algebra, numerical analysis, and computation.

Functional Data Analysis
Functional Data Analysis

Author: James Ramsay

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 317

ISBN-13: 147577107X

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Included here are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drawn from growth analysis, meteorology, biomechanics, equine science, economics, and medicine. The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, applied data analysts, and to experienced researchers; and as such is of value both within statistics and across a broad spectrum of other fields. Much of the material appears here for the first time.

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators

Author: Tailen Hsing

Publisher: John Wiley & Sons

Published: 2015-03-16

Total Pages: 368

ISBN-13: 1118762568

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Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). The self–contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self–adjoint and non self–adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis. This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.