Statistics for spatio-temporal data:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, NJ
Wiley
2011
|
| Series: | Wiley series in probability and statistics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XXII, 588 S. Ill., graph. Darst., Kt. |
| ISBN: | 9780471692744 |
Staff View
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| 245 | 1 | 0 | |a Statistics for spatio-temporal data |c Noel Cressie ; Christopher K. Wikle |
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| 300 | |a XXII, 588 S. |b Ill., graph. Darst., Kt. | ||
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Record in the Search Index
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| adam_text | Contents
Preface
xv
Acknowledgments
xix
1
Space-Time: The Next Frontier
1
t
2
Statistical Preliminaries
17
2.1
Conditional Probabilities and Hierarchical Modeling (HM),
20
2.1.1
Bayesian Hierarchical Modeling (BHM),
21
2.1.2
Empirical Hierarchical Modeling
(EHM),
23
2.1.3
Search for the USS Scorpion,
24
2.1.4
Classical Statistical Modeling,
27
2.1.5
Hierarchical Statistical Modeling,
28
2.2
Inference and Diagnostics,
33
2.2.1
Optimal Prediction,
36
2.2.2
Diagnostics,
39
2.3
Computation of the Posterior Distribution,
42
2.3.1
Simulation-Based Inference,
43
2.3.2
Markov Chain Monte Carlo (MCMC),
45
2.3.3
Summaries of the Posterior Distribution,
47
2.3.4
Additional Remarks,
47
2.4
Graphical Representations of Statistical Dependencies,
48
2.4.1
Directed and Undirected Graphs,
48
2.4.2
Conditional Independence,
50
2.4.3
Graphical Models for Temporal, Spatial, and
Spatio-Temporal
Processes,
52
2.5
Data/Model/Computing Compromises,
53
VII
VIH
CONTENTS
3
Fundamentals of Temporal Processes
55
3.1
Characterization of Temporal Processes,
56
3.1.1
Joint and Conditional Distributions,
57
3.1.2
Deterministic and Stochastic Processes,
58
3.2
Introduction to Deterministic Dynamical Systems,
59
3.2.1
Linear Discrete Dynamical Systems,
60
3.2.2
Nonlinear Discrete Dynamical Systems,
70
3.2.3
Bifurcation,
72
3.2.4
Chaos,
75
3.2.5
State-Space Reconstruction,
79
3.2.6
The Need for Statistical Models for Dynamical
Processes,
80
3.3
Time Series Preliminaries,
80
3.4
Basic Time Series Models,
84 ,
3.4.1
White-Noise Process,
84
3.4.2
Random-Walk Process,
85
3.4.3
Autoregressive
Process,
86
3.4.4
Moving-Average Process,
91 ·..
:
3.4.5
Autoregressive
Moving-Average Process,
94
3.4.6
Graphical Models for Time Series,
94
3.4.7
Vector
Autoregressive
Process,
95
3.4.8
Nonlinear Time-Series Models,
98
3.5
Spectral Representation ofTemporal Processes,
100
3.5.1
Spectral Representations via Orthogonal Series,
101
3.5.2
Discrete-Time Spectral Expansion,
103
3.5.3
Univariate Spectral Analysis,
107
3.5.4
Bivariate Spectral Analysis,
110
3.6
Hierarchical Modeling of Time Series,
112
3.7
Bibliographic Notes,
116
4
Fundamentals of Spatial Random Processes
119
4.1
Geostatistical Processes,
124
4.1.1
The Variogram and the Covariance Function,
125
4.1.2
Kriging (Optimal Spatial Prediction),
135
4.1.3
Change-of-Support,
149
4.1.4
Spatial Moving Average (SMA) Models,
152
4.1.5
Multivariate Geostatistical Processes,
158
4.1.6
Other Topics,
161
¿.л
CONTENTS
IX
4.2
Lattice
Processes,
167
4.2.1
Markov-Type
Models in Space, 171
4.2.2
The Markov Random Field (MRF),
174
4.2.3
From the Conditional Probabilities to the Joint
Probability,
181
4.2.4
Some Examples of Auto Spatial Models,
183
4.2.5
The CAR Model,
184
4.2.6
Hierarchical Modeling on a Spatial Lattice: Small Area
Estimation,
188
4.2.7
Simultaneously Specified Spatial Dependence,
198
4.2.8
Other Topics,
200
4.3
Spatial Point Processes,
204
4.3.1
The
Poisson
Point Process and the Cox Process,
207
4.3.2
Spatial Statistical Dependence, Including the
К
Function,
209
4.3.3
Distribution Theory for Spatial Point Processes,
215
4.3.4
Disease Mapping from Event Locations,
217
4.3.5
Other Topics,
220
4.4
Random Sets,
224
4.4.1
Hit-or-Miss Topology,
224
4.4.2
Hierarchical Models for Objects Based on Random
Sets,
226
4.4.3
The Boolean Model,
227
4.5
Bibliographic Notes,
231
5
Exploratory Methods for
Spatio-Temporal Data
243
5.1
Visualization,
244
5.1.1
Animations,
244
5.1.2
Marginal and Conditional Plots,
244
5.1.3
Empirical Covariance/Correlation Functions,
249
5.1.4
Spatio-Temporal
LISAs,
254
5.1.5
Spatio-Temporal
Parallel Coordinates Plot,
257
5.2
Spectral Analysis,
259
5.2.1
Cross-Spectral Analysis,
259
5.2.2
Spatio-Temporal
Spectral Analysis,
260
5.3
Empirical Orthogonal Function (EOF) Analysis,
266
5.3.1
Spatially Continuous Formulation,
266
5.3.2
Spatially Discrete Formulation,
268
5.3.3
Temporal Formulation,
269
X
CONTENTS
5.3.4
Calculation of EOFs,
269
5.4
Extensions of EOF Analysis,
271
5.4.1
Complex (Hubert).EOFs,
271
5.4.2
Multivariate EOF Analysis,
277
5.4.3
Extended EOF Analysis,
278
5.5
Principal Oscillation Patterns (POPs),
279
°
5.5.1
Calculation of POPs,
281
5.6
Spatio-Temporal
Canonical Correlation Analysis (CCA),
284
5.6.1
Two-Field
Spatio-Temporal
CCA,
284
5.6.2
Time-Lagged CCA,
290
5.7
Spatio-Temporal
Field Comparisons,
291
5.8
Bibliographic Notes,
292
6
Spatio-Temporal
Statistical Models
297
6.1
Spatio-Temporal Covariance
Functions,
304
6.1.1
Theoretical Properties,
307
6.1.2
Stationarity in Space or Time,
307
6.1.3
Separability and Full Symmetry,
309
6.1.4
Sums and Products of Covariance Functions,
315
6.1.5
Spatio-Temporal
Variogram,
315
6.1.6
Spectral Representation,
316
6.1.7
Taylor s Hypothesis,
319
6.1.8
Spatio-Temporal
Dynamical Models,
320
6.2
Spatio-Temporal Kriging,
321
6.2.1
Simple Kriging,
322
6.2.2
Ordinary Kriging,
324
6.2.3
Optimal Linear Prediction (Kriging) for Dynamical
Models,
327
6.3
Stochastic Differential and Difference Equations,
327
6.3.1
Spatio-Temporal
Statistical Properties,
327
6.3.2
The Diffusion-Injection Equation, Revisited,
332
6.4
Time Series of Spatial Processes,
336
6.4.1
Continuous Spatial Index (Geostatistical Processes),
336
6.4.2
Discrete Spatial Index (Lattice Processes),
341
6.5
Spatio-Ţemporal
Point Processes,
347
6.6
Spatio-Temporal
Components-of-Variation Models,
351
6.6.1
Dimension Reduction in a Dynamical Model,
353
6.6.2
Hierarchical
Spatio-Temporal
Statistical Modeling,
355
6.7
Bibliographic Notes,
356
CONTENTS Xl
7
Hierarchical Dynamical Spatio-Temporal Models
361
7.1
Data Models for the DSTM,
363
7.1.1
Linear Mappings with Equal Dimensions,
365
7.1.2
Linear Mappings with Unequal Dimensions,
366
7.1.3
Dimension Reduction,
369
7.1.4
Nonlinear Mappings,
379
7.1.5
Non-Gaussian Data Models,
380
7.2
Process Models for the DSTM: Linear Models,
382
7.2.1
Spatio-Temporal Random Walk,
385
7.2.2
Spatial
Autoregressive
Model,
385
7.2.3
Lagged Nearest-Neighbor Model,
386
7.2.4
PDE-Based
Paraméteri
zati
ons,
387
7.2.5
IDE-Based Dynamics,
392
7.2.6
Spectral Parameterizations,
396
7.2.7
Multiresolution DSTMs,
402
7.3
Process Models for the DSTM: Nonlinear Models,
403
7.3.1
Local Linear Approximations,
405
7.3.2
State-Dependent Processes,
406
7.3.3
General Quadratic Nonlinearity (GQN),
408
7.3.4
Agent-Based Models,
415
7.4
Process Models for the DSTM: Multivariate Models,
418
7.4.1
Augmenting the State Process (Multivariate DSTMs),
418
7.4.2
Dependence on Common Processes,
420
7.4.3
Conditional Formulation,
423
7.5
DSTM Parameter Models,
425
7.5.1
Data-Model Parameters,
426
7.5.2
Process-Model Parameters,
427
7.5.3
Deterministic Process Models with Random
Parameters,
429
7.6
Dynamical Design of Monitoring Networks,
430
7.7
Switching the Emphasis of Time and Space,
432
7.8
Bibliographic Notes,
433
8
Hierarchical DSTMs: Implementation and Inference
441
8.1
DSTM Process: General Implementation and Inference,
441
8.1.1
Sequential Implementation: General,
442
8.2
Inference for the DSTM Process: Linear/Gaussian
Models,
444
8.2.1
Kalman
Filter,
444
XU
CONTENTS
8.2.2
Kalman
Smoother,
449 - ; ·;:.
8.3
Inference for the DSTM Parameters: Linear/Gaussian
Models,
450 .,
<s
8.3.1
EHM
Implementation via the EM Algorithm,
451
8.3.2
BHM Implementation via the Gibbs Sampler,
454
8.4
Inference for the Hierarchical·DSTM: Nonlinear/Non-Gaussian
Models,
460
8.4.1
Extended
Kalman
Filter,
461
8.4.2
BHM Implementation via MCMC,
462 ;
8.4.3
Alternative Metropolis-Hastings Algorithms and
Approximations,
464
8.4.4
Importance Sampling Monte Carlo,
465
8.4.5
Sequential Monte Carlo,
468
8.4.6
Particle Filtering,
469
u
8.4.7
Ensemble
Kalman
Filtering,
470 *
8.4.8
Integrated Nested Laplace Approximation (INLA),
471
8.5
Bibliographic Notes,
472
9
Hierarchical DSTMs: Examples
475
9.1
Long-Lead Forecasting of Tropical Pacific Sea Surface
Temperatures,
476
9.1.1
Reduced-Dimension Linear DSTM,
477
9.1.2
Reduced-Dimension Nonlinear DSTM,
482
9.2
Remotely Sensed Aerosol Optical Depth,
488
і
-
9.2.1
AOD Data Model and Process Model,-
490
9.2.2
The AOD
EHM:
Implementation and Inference,
492
9.2.3
Predictions for Aerosol Optical Depth,
494
9.3
Modeling and Forecasting the Eurasian Collared Dove Invasion,
499
9.3.1
The Eurasian Collared Dove Invasion of North America,
499
9.3.2
ECD Data Model,
500
9.3.3
ECD Process Model,
502
9.3.4
ECD Parameter Model;
504
9.3.5
The ECD BHM: Implementation and Inference,
504
9.4
Mediterranean Surface Vector Winds,
507 .
9.4.1
Scientific Motivation for the Process Model,
509
9.4.2
TheSVWBHM,
510
9.4.3
The SVW BHM: Implementation and Inference,
514
CONTENTS
ХІІІ
Epilogue
519
References
523
Index
571
|
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| indexdate | 2024-07-09T22:55:29Z |
| institution | BVB |
| isbn | 9780471692744 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-021207443 |
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| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Cressie, Noel A. C. 1950- Verfasser (DE-588)112102530 aut Statistics for spatio-temporal data Noel Cressie ; Christopher K. Wikle Hoboken, NJ Wiley 2011 XXII, 588 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Literaturangaben Time-series analysis Spatial analysis (Statistics) Stochastischer Prozess (DE-588)4057630-9 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Räumliche Statistik (DE-588)4386767-4 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Stochastischer Prozess (DE-588)4057630-9 s Räumliche Statistik (DE-588)4386767-4 s DE-604 Wikle, Christopher K. 1963- Verfasser (DE-588)1011778769 aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021207443&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cressie, Noel A. C. 1950- Wikle, Christopher K. 1963- Statistics for spatio-temporal data Time-series analysis Spatial analysis (Statistics) Stochastischer Prozess (DE-588)4057630-9 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Räumliche Statistik (DE-588)4386767-4 gnd |
| subject_GND | (DE-588)4057630-9 (DE-588)4067486-1 (DE-588)4386767-4 |
| title | Statistics for spatio-temporal data |
| title_auth | Statistics for spatio-temporal data |
| title_exact_search | Statistics for spatio-temporal data |
| title_full | Statistics for spatio-temporal data Noel Cressie ; Christopher K. Wikle |
| title_fullStr | Statistics for spatio-temporal data Noel Cressie ; Christopher K. Wikle |
| title_full_unstemmed | Statistics for spatio-temporal data Noel Cressie ; Christopher K. Wikle |
| title_short | Statistics for spatio-temporal data |
| title_sort | statistics for spatio temporal data |
| topic | Time-series analysis Spatial analysis (Statistics) Stochastischer Prozess (DE-588)4057630-9 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Räumliche Statistik (DE-588)4386767-4 gnd |
| topic_facet | Time-series analysis Spatial analysis (Statistics) Stochastischer Prozess Zeitreihenanalyse Räumliche Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021207443&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cressienoelac statisticsforspatiotemporaldata AT wiklechristopherk statisticsforspatiotemporaldata |