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Functional Data Analysis with R and MATLAB

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 ...

Author : James Ramsay

Release : 2009-06-29

Publisher : Springer Science & Business Media

ISBN : 0387981853

File Size : 49.80 MB

Format : PDF, Mobi

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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

Wavelets in Functional Data Analysis

Data Anal. 55, 738–751 (2011) M. Nussbaum, Asymptotic equivalence of density
estimation and Gaussian white noise. ... (Springer, New York, 2006) J.O. Ramsay
, G. Hooker, S. Graves, Functional Data Analysis with R and MATLAB (Springer, ...

Author : Pedro A. Morettin

Release : 2017-11-07

Publisher : Springer

ISBN : 3319596233

File Size : 73.9 MB

Format : PDF

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Wavelet-based procedures are key in many areas of statistics, applied mathematics, engineering, and science. This book presents wavelets in functional data analysis, offering a glimpse of problems in which they can be applied, including tumor analysis, functional magnetic resonance and meteorological data. Starting with the Haar wavelet, the authors explore myriad families of wavelets and how they can be used. High-dimensional data visualization (using Andrews' plots), wavelet shrinkage (a simple, yet powerful, procedure for nonparametric models) and a selection of estimation and testing techniques (including a discussion on Stein’s Paradox) make this a highly valuable resource for graduate students and experienced researchers alike.

Introduction to Functional Data Analysis

(2009) provide a much more comprehensive and detailed description of the R
and MATLAB tools used for analysis of functional data. Our objective is to enable
the reader to perform simple analyses in R and gain some working
understanding ...

Author : Piotr Kokoszka

Release : 2017-09-27

Publisher : CRC Press

ISBN : 1498746691

File Size : 74.56 MB

Format : PDF

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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.

Spatial and Spatio-Temporal Geostatistical Modeling and Kriging

Porcu, E., Mateu, J., Zini, A. and Pini, R. (2007c) Modelling spatio-temporal data:
a new variogram and covariance structure proposal. Statistics and ... Ramsay, J.,
Hooker, G. and Graves, S. (2013) Functional Data Analysis with R and MATLAB.

Author : José-María Montero

Release : 2015-08-19

Publisher : John Wiley & Sons

ISBN : 1118762428

File Size : 24.33 MB

Format : PDF

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Statistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them. This book is a unified approach to modeling spatial and spatio-temporal data together with significant developments in statistical methodology with applications in R. This book includes: Methods for selecting valid covariance functions from the empirical counterparts that overcome the existing limitations of the traditional methods. The most innovative developments in the different steps of the kriging process. An up-to-date account of strategies for dealing with data evolving in space and time. An accompanying website featuring R code and examples

Yield Curve Modeling and Forecasting

Nyholm, K. (2008), Strategic Asset Allocation in Fixed-Income Markets: A Matlab-
Based User's Guide, Wiley. Nyholm, K. ... Ramsay, J.O., G. Hooker, and S. Graves
(2009), Functional Data Analysis with R and Matlab, Springer. Ramsay, J.O. ...

Author : Francis X. Diebold

Release : 2013-01-15

Publisher : Princeton University Press

ISBN : 1400845416

File Size : 28.53 MB

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Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

Functional Data Analysis for Online Auction Data

... mean 0.4 and variance 0.3 , to reflect the bathtub shape of the density of bid
arrival times as observed for auction data . ... Matlab code provided by the
authors , using default settings ; for KERNEL on the glkerns ( ) function from the
lokern R ...

Author : Bitao Liu

Release : 2008

Publisher :


File Size : 40.85 MB

Format : PDF, Kindle

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Journal of the American Statistical Association

Possibly a second edition of the text with a full R library would rent Business , 69 ,
40 – 41 . provide even easier access to their proposed methods . ... Ottawa : say
and Silverman ' s R / S - PLUS or Matlab libraries might also be interesting
Statistics Canada , available at www ... Part III , Nonparametric Classification of
Functional Functional data analysis ( FDA ) is an emerging area intended to
directly ...

Author : American Statistical Association

Release : 2007

Publisher :


File Size : 27.65 MB

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Contributions to Functional Data Analysis with Biological Applications

The length of stimuli function is 2220 . The length of time series is 1110 . The
length of estimated HRF is set as 18 . On an Intel ( R ) P4 2 . 80GHz , 1G memory
Linux computer , it takes about 8 hours to process one entire brain with our
Matlab ...

Author : Yuefeng Lu

Release : 2006

Publisher :


File Size : 88.92 MB

Format : PDF, Mobi

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Computational Statistics Handbook with MATLAB

Wendy L. Martinez, Angel R. Martinez. Chapter. 8. Probability. Density.
Estimation. 8.1 Introduction We discussed several techniques for graphical
exploratory data analysis in Chapter 5. ... Another way to gain understanding
about the distribution of the data is to estimate the probability density function
from the random ...

Author : Wendy L. Martinez

Release : 2001-09-26

Publisher : CRC Press

ISBN : 1420035630

File Size : 22.21 MB

Format : PDF, ePub

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Approaching computational statistics through its theoretical aspects can be daunting. Often intimidated or distracted by the theory, researchers and students can lose sight of the actual goals and applications of the subject. What they need are its key concepts, an understanding of its methods, experience with its implementation, and practice with

Numerical Analysis and Graphic Visualization with MATLAB

Extended trapezoidal rule ( 2 ) Purpose : Integration of a function by extended
trapezoidal rule . ... ( f _ name , xc ) ; plot ( xc , fc , ' r ' ) ; hold on title ( ' Trapezoidal
Rule ' ) ; xlabel ( ' x ' ) ; ylabel ( ' y ' ) ; plot ( x , f ) ; plot ( x , zeros ( size ( x ) ) ) for i =
1 : n ; plot ( [ x ( i ) , x ( i ) ] , [ 0 , f ( i ) ] ) ; end FM 5 - 3 . ... a , b , n ) f name : function
m - file name of the integrand , f ( x ) f : functional data on equispaced abscissa .

Author : Shoichiro Nakamura

Release : 2002

Publisher : Prentice Hall


File Size : 86.53 MB

Format : PDF, ePub, Docs

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Leverage the power of MATLAB 6 in all your technical computation and measurement applications Now, there is a complete introduction to numerical methods and visualization with the latest, most powerful version of MATLAB, Version 6.0. Dr. Shoichiro Nakamura introduces the skills and knowledge needed to solve numerical equations with MATLAB, understand the computational results, and present them graphically. This book brings together all four cornerstones of numerical analysis with MATLAB: the fundamental techniques of MATLAB programming; the mathematical basis of numerical methods; the application of numerical analysis to engineering, scientific, and mathematical problems; and the creation of scientific graphics. Coverage includes: Complete introductory tutorials for both MATLAB 6.0 programming and professional-quality 3D graphics Linear algebra applications: matrices, vectors, Gauss elimination, Gauss-Jordan elimination, LU decomposition, and more Polynomials and interpolation, including interpolation with Chebyshev points; cubic hermite, 2D and transfinite interpolation; and M-files Numerical integration, differentiation, and roots of nonlinear equations Advanced techniques, including curve fitting, spline functions, and boundary value problems Whether you are a student, engineer, scientist, researcher, or economic analyst, MATLAB 6 offers you unprecedented power for defining and solving problems. Put that power to work -- with Numerical Analysis and Graphical Visualization with MATLAB, second edition.

Computational Methods in Science and Engineering

CONCLUSIONS In this article we compared two analysis - of - variance models in
terms of their capacity for detecting ... our gratitude to Professor J. O. Ramsay and
his team for the functional data analysis software for Matlab that served as the ...
Cuevas , A. , Febrero , M. and Fraiman , R. , An ANOVA test for functional data .

Author : George Maroulis

Release : 2009-09-02

Publisher : Amer Inst of Physics


File Size : 56.96 MB

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The aim of ICCMSE 2008 is to bring together computational scientists and engineers from several disciplines in order to share methods, methodologies and ideas. The potential readers are all the scientists with interest in: Computational Mathematics, Theoretical Physics, Computational Physics, Theoretical Chemistry, Computational Chemistry, Mathematical Chemistry, Computational Engineering, Computational Mechanics, Computational Biology and Medicine, Scientific Computation, High Performance Computing, Parallel and Distributed Computing, Visualization, Problem Solving Environments, Software Tools, Advanced Numerical Algorithms, Modelling and Simulation of Complex Systems, Web-based Simulation and Computing, Grid-based Simulation and Computing, Computational Grids, and Computer Science.

Data Literacy

But data visualization has been used for decades as a means to help understand
data patterns and communicate those to others. The goal ... Some statistical
programs, such as SPSS, JMP, MATLAB and the open source R, have
exploratory data analysis functions. ... To learn more, read The Functional Art by
Alberto Cairo (2013), Visualize This by Nathan Yau (2011), or the books of
Edward Tufte (1983, ...

Author : David Herzog

Release : 2015-01-29

Publisher : SAGE Publications

ISBN : 1483378659

File Size : 47.8 MB

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A practical, skill-based introduction to data analysis and literacy We are swimming in a world of data, and this handy guide will keep you afloat while you learn to make sense of it all. In Data Literacy: A User's Guide, David Herzog, a journalist with a decade of experience using data analysis to transform information into captivating storytelling, introduces students and professionals to the fundamentals of data literacy, a key skill in today’s world. Assuming the reader has no advanced knowledge of data analysis or statistics, this book shows how to create insight from publicly-available data through exercises using simple Excel functions. Extensively illustrated, step-by-step instructions within a concise, yet comprehensive, reference will help readers identify, obtain, evaluate, clean, analyze and visualize data. A concluding chapter introduces more sophisticated data analysis methods and tools including database managers such as Microsoft Access and MySQL and standalone statistical programs such as SPSS, SAS and R.

Stochastic Geometry, Spatial Statistics and Random Fields

This volume is an attempt to provide a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, with special emphasis placed on fundamental classes of models and algorithms as well as on ...

Author : Volker Schmidt

Release : 2014-10-24

Publisher : Springer

ISBN : 3319100645

File Size : 44.49 MB

Format : PDF, Docs

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This volume is an attempt to provide a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, with special emphasis placed on fundamental classes of models and algorithms as well as on their applications, e.g. in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R which are widely used in the mathematical community. It can be seen as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered with a focus on asymptotic methods.


function . Bootstrapping. from. residuals. ,. confidence. intervals. for. parametric.
functions , bootstrapping in the ... Chapter 18 , “ Imputing Missing Data , "
proposes single stochastic imputation for missing data and use of the " completed
" dataset for inference . ... themselves , a website is available that contains many
of the datasets and analyses done with MATLAB or S - PLUS . ... Gnanadesikan ,
R . ( 1997 ) , Methods for Statistical Data Analysis of Multivariate Observations (
2nd ed . ) ...

Author :

Release : 2003

Publisher :


File Size : 24.35 MB

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Amstat News

Discrete Multistage Decision Networks • Learning About Probabilities • Checking
Models Against Data Structure Learning ... is updating for the current versions of
S - PLUS and adding coverage of R . Contents : Introduction Data Manipulation .
ANALYSIS Methods and Case Studies J . O ... There is an associated web site
with MATLAB and S - PLUS implementations of the methods discussed , together
with all ...

Author : American Statistical Association

Release : 2002

Publisher :


File Size : 64.3 MB

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Fundamentals of Food Biotechnology

The data should be provided in a clear, consistent manner with some
visualization tools to aid biological interpretation. ... data analysis tools that fall
into the following four major categories: (i) similarity searching tools, (ii) protein
function analysis, ... Commonly used software tools and technologies in this field
include Java, XML, Perl, C, C++, Python, R, MySQL, SQL, CUDA, MATLAB, and
Microsoft Excel.

Author : Byong H. Lee

Release : 2014-12-01

Publisher : John Wiley & Sons

ISBN : 1118384911

File Size : 88.67 MB

Format : PDF

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Food biotechnology is the application of modern biotechnologicaltechniques to the manufacture and processing of food, for examplethrough fermentation of food (which is the oldest biotechnologicalprocess) and food additives, as well as plant and animal cellcultures. New developments in fermentation and enzyme technologicalprocesses, molecular thermodynamics, genetic engineering, proteinengineering, metabolic engineering, bioengineering, and processesinvolving monoclonal antibodies, nanobiotechnology and quorumsensing have introduced exciting new dimensions to foodbiotechnology, a burgeoning field that transcends many scientificdisciplines. Fundamentals of Food Biotechnology, 2nd edition is basedon the author’s 25 years of experience teaching on a foodbiotechnology course at McGill University in Canada. The book willappeal to professional food scientists as well as graduate andadvanced undergraduate students by addressing the latest excitingfood biotechnology research in areas such as genetically modifiedfoods (GMOs), bioenergy, bioplastics, functionalfoods/nutraceuticals, nanobiotechnology, quorum sensing andquenching. In addition, cloning techniques for bacterial and yeastenzymes are included in a “New Trends and Tools”section and selected references, questions and answers appear atthe end of each chapter. This new edition has been comprehensively rewritten andrestructured to reflect the new technologies, products and trendsthat have emerged since the original book. Many new aspectshighlight the short and longer term commercial potential of foodbiotechnology.

Signals and Systems with MATLAB

This book is primarily intended for junior-level students who take the courses on ‘signals and systems’.

Author : Won Young Yang

Release : 2009-06-18

Publisher : Springer Science & Business Media

ISBN : 3540929541

File Size : 23.59 MB

Format : PDF, ePub, Mobi

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This book is primarily intended for junior-level students who take the courses on ‘signals and systems’. It may be useful as a reference text for practicing engineers and scientists who want to acquire some of the concepts required for signal proce- ing. The readers are assumed to know the basics about linear algebra, calculus (on complex numbers, differentiation, and integration), differential equations, Laplace R transform, and MATLAB . Some knowledge about circuit systems will be helpful. Knowledge in signals and systems is crucial to students majoring in Electrical Engineering. The main objective of this book is to make the readers prepared for studying advanced subjects on signal processing, communication, and control by covering from the basic concepts of signals and systems to manual-like introduc- R R tions of how to use the MATLAB and Simulink tools for signal analysis and lter design. The features of this book can be summarized as follows: 1. It not only introduces the four Fourier analysis tools, CTFS (continuous-time Fourier series), CTFT (continuous-time Fourier transform), DFT (discrete-time Fourier transform), and DTFS (discrete-time Fourier series), but also illuminates the relationship among them so that the readers can realize why only the DFT of the four tools is used for practical spectral analysis and why/how it differs from the other ones, and further, think about how to reduce the difference to get better information about the spectral characteristics of signals from the DFT analysis.

Modeling Variation in Functional Responses with Applications in Human Motion Analysis

We then looked at various approaches for examining the variability in functional
data . First , functional principal components analysis was applied to the right
elbow angle data , where we found the effect of the respective first principal ...

Author : Jennifer S. Hu

Release : 2005

Publisher :


File Size : 55.18 MB

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Drug Discovery in Pancreatic Cancer

It is clear even from the copy number data that there are several genes that play a
dominant role in carcinogenesis including ... to carry out conventional gene-
functional validation studies Now that high-throughput technologies can be
applied to validation, ... Proc Natl Acad Sci U S A 101:9077–9072 Autio R,
Hautaniemi S, Kauraniemi Petal (2003) CGH-Plotter: MATLAB toolbox for CGH-
data analysis.

Author : Haiyong Han

Release : 2010-03-11

Publisher : Springer Science & Business Media

ISBN : 9781441911605

File Size : 67.17 MB

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Pancreatic cancer is the fourth leading cause of cancer death in the United States. Every year, about 33,700 people in the United States will be diagnosed with pancreatic cancer and over 32,000 patients will die from the disease. The median survival of patients with advanced pancreatic cancer is about 6-months. This dismal picture of pancreatic cancer is mainly due to the lack of early diagnosis and effective treatment for patients with advanced disease. To increase the survival rate of pancreatic cancer patients, better tumor markers for diagnosis and new molecular targets for drug development are desperately needed. A lot of effort has been made in searching for pancreatic cancer-causing genes or genes associated with progression of malignant behavior in pancreatic cancer. As a result, alterations in the expression of several cancer-related genes have been identified in pancreatic tumors. The identification and characterization of these cancer-related genes have significantly increased our understanding of pancreatic cancer development, but unfortunately the treatment of pancreatic cancer has not advanced as much in the past 20 years. Over the past decade, tremendous advances have been made in the field of cancer drug discovery, particularly, in the area of molecular and genetic models and technologies. Many of those advanced models and technologies have been applied to the drug discovery processes for pancreatic cancer. In this book, a team of experts will describe the latest development in the application of these models and technologies in pancreatic cancer. The authors include basic researchers as well as clinicians who work in the front-line of the war against pancreatic cancer and have the first-hand experience on these cutting-edge tools and techniques. The book can be divided into two general areas: 1) model systems and 2) genomics and proteomics tools. In recent years there have been a lot of advances in the model systems for pancreatic cancer, including the further characterization of normal and cancerous pancreatic cell lines, the establishment of transgenic mouse models that recapitulate the initiation and progression of human pancreatic cancer, the development of a new xenograft model system for the evaluation of novel agents, and the establishment of a zebrafish pancreatic cancer model. The first four chapters of the book will be devoted to these models. The advances in genomics and proteomics research have made a major impact in cancer drug discovery. A number of these –omics-based tools and techniques have been applied in the pancreatic cancer drug discovery. Chapters 5-9 of the book will discuss techniques for genome-wide examination of gene expression, copy number, methylation, function and regulation. Chapters 10-11 will discuss in situ techniques for studying chromosomal and gene copy number abnormalities as well protein expression changes in cancer samples. Chapters 12-14 will focus on techniques for global examination of protein expression levels in biospecimens obtained from pancreatic cancer patients. Cancer drug discovery has become more and more target-centric.