Spatiotemporal data analysis /:
"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 an...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Princeton :
Princeton University Press,
[2012]
|
| Subjects: | |
| Online Access: | Volltext |
| Summary: | "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"-- |
| Physical Description: | 1 online resource (xvi, 317 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 1400840635 9781400840632 1306661412 9781306661416 |
Staff View
MARC
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| 100 | 1 | |a Eshel, Gidon, |d 1958- |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjFP87f6jG6G6j3dkTjw83 |0 http://id.loc.gov/authorities/names/n2011062154 | |
| 245 | 1 | 0 | |a Spatiotemporal data analysis / |c Gidon Eshel. |
| 264 | 1 | |a Princeton : |b Princeton University Press, |c [2012] | |
| 264 | 4 | |c ©2012 | |
| 300 | |a 1 online resource (xvi, 317 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a "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"-- |c Provided by publisher | ||
| 505 | 0 | |a 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 | |a 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 | |a 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 | |a 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 | |a 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. | |
| 650 | 0 | |a Spatial analysis (Statistics) |0 http://id.loc.gov/authorities/subjects/sh85126347 | |
| 650 | 6 | |a Analyse spatiale (Statistique) | |
| 650 | 7 | |a spatial analysis. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Earth Sciences |x General. |2 bisacsh | |
| 650 | 7 | |a Spatial analysis (Statistics) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Eshel, Gidon, 1958- |t Spatiotemporal data analysis. |d Princeton, N.J. : Princeton University Press, ©2012 |z 9780691128917 |w (DLC) 2011032275 |w (OCoLC)724663242 |
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|---|---|
| _version_ | 1816882263933059072 |
| adam_text | |
| any_adam_object | |
| author | Eshel, Gidon, 1958- |
| author_GND | http://id.loc.gov/authorities/names/n2011062154 |
| author_facet | Eshel, Gidon, 1958- |
| author_role | aut |
| author_sort | Eshel, Gidon, 1958- |
| author_variant | g e ge |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QA278 |
| callnumber-raw | QA278.2 .E84 2012 |
| callnumber-search | QA278.2 .E84 2012 |
| callnumber-sort | QA 3278.2 E84 42012 |
| callnumber-subject | QA - Mathematics |
| collection | ZDB-4-EBA |
| contents | 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. 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. 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. 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. 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. |
| ctrlnum | (OCoLC)872694292 |
| dewey-full | 519.5/36 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 |
| dewey-search | 519.5/36 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Spatial analysis (Statistics)</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85126347</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Analyse spatiale (Statistique)</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">spatial analysis.</subfield><subfield code="2">aat</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MATHEMATICS</subfield><subfield code="x">Applied.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MATHEMATICS</subfield><subfield code="x">Probability & Statistics</subfield><subfield code="x">General.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SCIENCE</subfield><subfield code="x">Earth Sciences</subfield><subfield code="x">General.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Spatial analysis (Statistics)</subfield><subfield code="2">fast</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Eshel, Gidon, 1958-</subfield><subfield code="t">Spatiotemporal data analysis.</subfield><subfield code="d">Princeton, N.J. : Princeton University Press, ©2012</subfield><subfield code="z">9780691128917</subfield><subfield code="w">(DLC) 2011032275</subfield><subfield code="w">(OCoLC)724663242</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=761008</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="936" ind1=" " ind2=" "><subfield code="a">BATCHLOAD</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Askews and Holts Library Services</subfield><subfield code="b">ASKH</subfield><subfield code="n">AH26388032</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBL - Ebook Library</subfield><subfield code="b">EBLB</subfield><subfield code="n">EBL832068</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">ebrary</subfield><subfield code="b">EBRY</subfield><subfield code="n">ebr10862955</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">761008</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">ProQuest MyiLibrary Digital eBook Collection</subfield><subfield code="b">IDEB</subfield><subfield code="n">cis28123212</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">7310782</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBA-ocn872694292 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:25:50Z |
| institution | BVB |
| isbn | 1400840635 9781400840632 1306661412 9781306661416 |
| language | English |
| oclc_num | 872694292 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xvi, 317 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Princeton University Press, |
| record_format | marc |
| spelling | Eshel, Gidon, 1958- author. https://id.oclc.org/worldcat/entity/E39PCjFP87f6jG6G6j3dkTjw83 http://id.loc.gov/authorities/names/n2011062154 Spatiotemporal data analysis / Gidon Eshel. Princeton : Princeton University Press, [2012] ©2012 1 online resource (xvi, 317 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Print version record. "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"-- Provided by publisher 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. 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. 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. 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. 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. Spatial analysis (Statistics) http://id.loc.gov/authorities/subjects/sh85126347 Analyse spatiale (Statistique) spatial analysis. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh SCIENCE Earth Sciences General. bisacsh Spatial analysis (Statistics) fast Print version: Eshel, Gidon, 1958- Spatiotemporal data analysis. Princeton, N.J. : Princeton University Press, ©2012 9780691128917 (DLC) 2011032275 (OCoLC)724663242 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=761008 Volltext |
| spellingShingle | Eshel, Gidon, 1958- Spatiotemporal data analysis / 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. 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. 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. 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. 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. Spatial analysis (Statistics) http://id.loc.gov/authorities/subjects/sh85126347 Analyse spatiale (Statistique) spatial analysis. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh SCIENCE Earth Sciences General. bisacsh Spatial analysis (Statistics) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85126347 |
| title | Spatiotemporal data analysis / |
| title_auth | Spatiotemporal data analysis / |
| title_exact_search | Spatiotemporal data analysis / |
| title_full | Spatiotemporal data analysis / Gidon Eshel. |
| title_fullStr | Spatiotemporal data analysis / Gidon Eshel. |
| title_full_unstemmed | Spatiotemporal data analysis / Gidon Eshel. |
| title_short | Spatiotemporal data analysis / |
| title_sort | spatiotemporal data analysis |
| topic | Spatial analysis (Statistics) http://id.loc.gov/authorities/subjects/sh85126347 Analyse spatiale (Statistique) spatial analysis. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh SCIENCE Earth Sciences General. bisacsh Spatial analysis (Statistics) fast |
| topic_facet | Spatial analysis (Statistics) Analyse spatiale (Statistique) spatial analysis. MATHEMATICS Applied. MATHEMATICS Probability & Statistics General. SCIENCE Earth Sciences General. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=761008 |
| work_keys_str_mv | AT eshelgidon spatiotemporaldataanalysis |