LEADER 00000cam a2200709Ma 4500
001 ocn872694292
003 OCoLC
005 20160527040700.4
006 m o d
007 cr |n|||||||||
008 131104s2012 njua ob 001 0 eng d
019 769927219
020 1400840635|q(electronic book)
020 9781400840632|q(electronic book)
020 1306661412
020 9781306661416
020 |z9780691128917|q(hardback)
020 |z069112891X|q(hardback)
024 8 6825917
035 (OCoLC)872694292|z(OCoLC)769927219
037 22573/ctt7182rj|bJSTOR
040 CNSPO|beng|epn|cCNSPO|dOCLCO|dN$T|dIDEBK|dCCO|dOCLCF
|dJSTOR|dDEBSZ|dYDXCP|dK6U|dEBLCP|dE7B|dOCLCQ|dCUS|dOCLCQ
049 RIDW
050 4 QA278.2|b.E84 2012
072 7 MAT|x003000|2bisacsh
072 7 MAT|x029000|2bisacsh
072 7 SCI019000|2bisacsh
072 7 SCI092000|2bisacsh
072 7 MAT002050|2bisacsh
082 04 519.5/36|223
084 SCI019000|aMAT002050|2bisacsh
090 QA278.2|b.E84 2012
100 1 Eshel, Gidon,|d1958-|0https://id.loc.gov/authorities/names
/n2011062154|eauthor.
245 10 Spatiotemporal data analysis /|cGidon Eshel.
264 1 Princeton :|bPrinceton University Press,|c[2012]
264 4 |c©2012
300 1 online resource (xvi, 317 pages) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file|2rdaft
504 Includes bibliographical references and index.
505 0 Cover; Spatiotemporal Data Analysis; Title; Copyright;
Dedication; Contents; Preface; Acknowledgments; PART 1.
FOUNDATIONS; ONE Introduction and Motivation; TWO Notation
and Basic Operations; THREE Matrix Properties, Fundamental
Spaces, Orthogonality; 3.1 Vector Spaces; 3.2 Matrix Rank;
3.3 Fundamental Spaces Associated with AÎR M x N; 3.4 Gram
-Schmidt Orthogonalization; 3.5 Summary; FOUR Introduction
to Eigenanalysis; 4.1 Preface; 4.2 Eigenanalysis
Introduced; 4.3 Eigenanalysis as Spectral Representation;
4.4 Summary; FIVE The Algebraic Operation of SVD; 5.1 SVD
Introduced; 5.2 Some Examples.
505 8 5.3 SVD Applications5.4 Summary; PART 2. METHODS OF DATA
ANALYSIS; SIX The Gray World of Practical Data Analysis:
An Introduction to Part 2; SEVEN Statistics in
Deterministic Sciences: An Introduction; 7.1 Probability
Distributions; 7.2 Degrees of Freedom; EIGHT
Autocorrelation; 8.1 Theoretical Autocovariance and
Autocorrelation Functions of AR(1) and AR(2); 8.2 Acf-
Derived Timescale; 8.3 Summary of Chapters 7 and 8; NINE
Regression and Least Squares; 9.1 Prologue; 9.2 Setting Up
the Problem; 9.3 The Linear System Ax = b; 9.4 Least
Squares: The SVD View.
505 8 9.5 Some Special Problems Giving Rise to Linear Systems9.6
Statistical Issues in Regression Analysis; 9.7
Multidimensional Regression and Linear Model
Identification; 9.8 Summary; TEN. THE FUNDAMENTAL THEOREM
OF LINEAR ALGEBRA; 10.1 Introduction; 10.2 The Forward
Problem; 10.3 The Inverse Problem; ELEVEN. EMPIRICAL
ORTHOGONAL FUNCTIONS; 11.1 Introduction; 11.2 Data Matrix
Structure Convention; 11.3 Reshaping Multidimensional Data
Sets for EOF Analysis; 11.4 Forming Anomalies and Removing
Time Mean; 11.5 Missing Values, Take 1; 11.6 Choosing and
Interpreting the Covariability Matrix.
505 8 11.7 Calculating the EOFs11.8 Missing Values, Take 2; 11.9
Projection Time Series, the Principal Components; 11.10 A
Final Realistic and Slightly Elaborate Example: Southern
New York State Land Surface Temperature; 11.11 Extended
EOF Analysis, EEOF; 11.12 Summary; TWELVE. THE SVD
ANALYSIS OF TWO FIELDS; 12.1 A Synthetic Example; 12.2 A
Second Synthetic Example; 12.3 A Real Data Example; 12.4
EOFs as a Prefilter to SVD; 12.5 summary; THIRTEEN.
SUGGESTED HOMEWORK; 13.1 Homework 1, Corresponding to
Chapter 3; 13.2 Homework 2, Corresponding to Chapter 3.
505 8 13.3 Homework 3, Corresponding to Chapter 313.4 Homework 4,
Corresponding to Chapter 4; 13.5 Homework 5, Corresponding
to Chapter 5; 13.6 Homework 6, Corresponding to Chapter 8;
13.7 A Suggested Midterm Exam; 13.8 A Suggested Final
Exam; Index.
520 "A severe thunderstorm morphs into a tornado that cuts a
swath of destruction through Oklahoma. How do we study the
storm's mutation into a deadly twister? Avian flu cases
are reported in China. How do we characterize the spread
of the flu, potentially preventing an epidemic? The way to
answer important questions like these is to analyze the
spatial and temporal characteristics--origin, rates, and
frequencies--of these phenomena. This comprehensive text
introduces advanced undergraduate students, graduate
students, and researchers to the statistical and algebraic
methods used to analyze spatiotemporal data in a range of
fields, including climate science, geophysics, ecology,
astrophysics, and medicine. Gidon Eshel begins with a
concise yet detailed primer on linear algebra, providing
readers with the mathematical foundations needed for data
analysis. He then fully explains the theory and methods
for analyzing spatiotemporal data, guiding readers from
the basics to the most advanced applications. This self-
contained, practical guide to the analysis of
multidimensional data sets features a wealth of real-world
examples as well as sample homework exercises and
suggested exams"--|cProvided by publisher.
588 0 Print version record.
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
650 0 Spatial analysis (Statistics)|0https://id.loc.gov/
authorities/subjects/sh85126347
650 7 Spatial analysis (Statistics)|2fast|0https://
id.worldcat.org/fast/1128784
655 0 Electronic books.
655 4 Electronic books.
776 08 |iPrint version:|aEshel, Gidon, 1958-|tSpatiotemporal data
analysis.|dPrinceton, N.J. : Princeton University Press,
©2012|z9780691128917|w(DLC) 2011032275|w(OCoLC)724663242
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=761008|zOnline eBook. Access restricted to
current Rider University students, faculty, and staff.
856 42 |3Instructions for reading/downloading this eBook|uhttp://
guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d20160607|cEBSCO|tebscoebooksacademic|lridw
994 92|bRID
