Climate time series analysis: classical statistical and bootstrap methods
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht [u.a.]
Springer
2010
|
| Schriftenreihe: | Atmospheric and oceanographic sciences library
42 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XXXIV, 474 S. graph. Darst. |
| ISBN: | 9789048194810 9789048194827 |
Internformat
MARC
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| 100 | 1 | |a Mudelsee, Manfred |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Climate time series analysis |b classical statistical and bootstrap methods |c Manfred Mudelsee |
| 264 | 1 | |a Dordrecht [u.a.] |b Springer |c 2010 | |
| 300 | |a XXXIV, 474 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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Datensatz im Suchindex
| _version_ | 1805094196906819584 |
|---|---|
| adam_text |
Contents
Preface
vii
Acknowledgements
xi
List of Algorithms
xxi
List of Figures
xxv
List of Tables
xxxi
Part I Fundamental Concepts
1.
Introduction
3
1.1
Climate archives, variables and dating
5
1.2
Noise and statistical distribution
б
1.3
Persistence
11
1.4
Spacing
14
1.5
Aim and structure of this book
24
1.6
Background material
26
2.
Persistence Models
33
2.1
First-order
autoregressive
model
33
2.1.1
Even spacing
34
2.1.1.1
Effective data size
36
2.1.2
Uneven spacing
37
2.1.2.1
Embedding in continuous time
38
2.2
Second-order
autoregressive
model
39
2.3
Mixed
autoregressive
moving average model
41
2.4
Other models
42
2.4.1
Long-memory processes
42
xiii
Contents
2.4.2
Nonlinear and non-Gaussian models
43
2.5
Climate theory
44
2.5.1
Stochastic climate models
45
2.5.2
Long memory of temperature fluctuations?
47
2.5.3
Long memory of river runoff
51
2.6
Background material
54
2.7
Technical issues
63
3.
Bootstrap Confidence Intervals
65
3.1
Error bars and confidence intervals
66
3.1.1
Theoretical example
:
mean estimation of Gaussian
white noise
68
3.1.2
Theoretical example: standard deviation
estimation of Gaussian white noise
69
3.1.3
Real world
71
3.2
Bootstrap principle
74
3.3
Bootstrap resampling
76
3.3.1
Nonparametric: moving block bootstrap
78
3.3.1.1
Block length selection
78
3.3.1.2
Uneven spacing
80
3.3.1.3
Systematic model parts and
nonstationarity
81
3.3.2
Parametric:
autoregressive
bootstrap
83
3.3.2.1
Even spacing
83
3.3.2.2
Uneven spacing
83
3.3.3
Parametric: surrogate data
86
3.4
Bootstrap confidence intervals
86
3.4.1
Normal confidence interval
87
3.4.2
Student's
t
confidence interval
88
3.4.3
Percentile confidence interval
88
3.4.4
BCa confidence interval
88
3.5
Examples
89
3.6
Bootstrap hypothesis tests
91
3.7
Notation
94
3.8
Background material
99
3.9
Technical issues
106
Contents xv
Part II
Univariate
Time Series
4.
Regression I
113
4.1
Lineai
:
regression
114
4.1.1
Weighted least-squares and ordinary least-squares
estimation
114
4.1.1.1
Example: Arctic river runoff
115
4.1.2
Generalized least-squares estimation
116
4.1.3
Other estimation types
118
4.1.4
Classical confidence intervals
119
4.1.4.1
Prais-Winsten procedure
121
4.1.4.2
Cochrane-Orcutt transformation
121
4.1.4.3
Approach via effective data size
123
4.1.5
Bootstrap confidence intervals
124
4.1.6
Monte Carlo experiments: ordinary least-squares
estimation
124
4.1.7
Timescale errors
129
4.1.7.1
Nonparametric: pairwise-moving block
bootstrap
131
4.1.7.2
Parametric: timescale-autoregressive
bootstrap
131
4.1.7.3
Hybrid: timescale-moving block
bootstrap
136
4.1.7.4
Monte Carlo experiments
136
4.2
Nonlinear regression
141
4.2.1
Climate transition model: ramp
142
4.2.1.1
Estimation
143
4.2.1.2
Example: Northern Hemisphere
Glaciation
145
4.2.1.3
Bootstrap confidence intervals
146
4.2.1.4
Example: onset of Dansgaard-Oeschger
event
5
146
4.2.2
Trend-change model: break
150
4.2.2.1
Estimation
150
4.2.2.2
Example: Arctic river runoff
(continued)
152
4.2.2.3
Bootstrap confidence intervals
152
4.3
Nonparametric regression or smoothing
153
4.3.1
Kernel estimation
153
4.3.2
Bootstrap confidence intervals and bands
156
xvi Contents
4.3.3
Extremes or outlier detection
157
4.3.3.1
Example: volcanic peaks in the NGRIP
sulfate
record
159
4.3.3.2
Example: hurricane peaks in the Lower
Mystic Lake varve thickness record
159
4.4
Background material
161
4.5
Technical issues
173
5.
Spectral Analysis
177
5.1
Spectrum
177
5.1.1
Example: AR(1) process, discrete time
180
5.1.2
Example: AR(2) process, discrete time
180
5.1.3
Physical meaning
181
5.2
Spectral estimation
183
5.2.1
Periodogram
183
5.2.2
Welch's Overlapped Segment Averaging
186
5.2.3
Multitaper estimation
188
5.2.3.1
F
test
190
5.2.3.2
Weighted eigenspectra
191
5.2.3.3
Zero padding
192
5.2.3.4
Jackknife
192
5.2.3.5
Advanced topics:
CI
coverage accuracy
and uneven spacing
194
5.2.3.6
Example: radiocarbon spectrum
195
5.2.4
Lomb-Scargle estimation
196
5.2.4.1
Bias correction
197
5.2.4.2
Covariance
199
5.2.4.3
Harmonic filter
199
5.2.4.4
Advanced topics: degrees of freedom,
bandwidth, oversampling and highest
frequency
201
5.2.5
Peak detection: red-noise hypothesis
202
5.2.5.1
Multiple tests
202
5.2.6
Example: peaks in monsoon spectrum
205
5.2.7
Aliasing
205
5.2.8
Timescale errors
208
5.2.9
Example: peaks in monsoon spectrum
(continued)
209
5.3
Background material
215
5.4
Technical issues
225
Contents xvii
6.
Ex
6.1
6.2
6.3
reme
Value Time Series
229
Data
types
229
6.1.1
Event times
230
6.1.1.1
Example: Elbe winter floods
230
6.1.2
Peaks over threshold
230
6.1.2.1
Example: volcanic peaks in the NGRIP
sulfate
record (continued)
231
6.1.3
Block extremes
231
6.1.4
Remarks on data selection
232
Stationary models
232
6.2.1
Generalized Extreme Value distribution
232
6.2.1.1
Model
233
6.2.1.2
Maximum likelihood estimation
233
6.2.2
Generalized Pareto distribution
235
6.2.2.1
Model
235
6.2.2.2
Maximum likelihood estimation
235
6.2.2.3
Model suitability
237
6.2.2.4
Return period
238
6.2.2.5
Probability weighted moment
estimation
239
6.2.3
Bootstrap confidence intervals
240
6.2.4
Example: Elbe summer floods,
1852
to
2002
241
6.2.5
Persistence
243
6.2.5.1
Condition D(un)
243
6.2.5.2
Extremal index
243
6.2.5.3
Long memory
244
6.2.6
Remark: tail estimation
244
6.2.7
Remark: optimal estimation
246
Nonstationary
models
246
6.3.1
Time-dependent Generalized Extreme Value
distribution
247
6.3.2
Inhomogeneous
Poisson
process
248
6.3.2.1
Model
248
6.3.2.2
Nonparametric occurrence rate
estimation
249
6.3.2.3
Boundary bias reduction
250
6.3.2.4
Bandwidth selection
251
6.3.2.5
Example: Elbe winter floods
(continued)
252
xviü
Contents
6.3.2.6
Bootstrap confidence band
253
6.3.2.7
Example: Elbe winter floods
(continued)
256
6.3.2.8
Example: volcanic peaks in the NGRIP
sulfate
record (continued)
257
6.3.2.9
Example: hurricane peaks in the Lower
Mystic Lake varve thickness record
(continued)
257
6.3.2.10
Parametric
Poisson
models and
hypothesis tests
258
6.3.2.11
Monte Carlo experiment
:
Cox-Lewis test
versus Mann-Kendall test
260
6.3.3
Hybrid: Poisson-extreme value distribution
264
6.4
Sampling and time spacing
266
6.5
Background material
269
6.6
Technical issues
279
Part III Bivariate Time Series
7.
Correlation
285
7.1
Pearson's correlation coefficient
286
7.1.1
Remark: alternative correlation measures
287
7.1.2
Classical confidence intervals, non-persistent
processes
287
7.1.3
Bivariate time series models
289
7.1.3.1
Bivariate white noise
289
7.1.3.2
Bivariate first-order
autoregressive
process
290
7.1.4
Classical confidence intervals, persistent processes
291
7.1.5
Bootstrap confidence intervals
293
7.1.5.1
Pairwise-moving block bootstrap
293
7.1.5.2
Pairwise-autoregressive bootstrap
295
7.2
Spearman's rank correlation coefficient
295
7.2.1
Classical confidence intervals, non-persistent
processes
298
7.2.2
Classical confidence intervals, persistent processes
300
7.2.3
Bootstrap confidence intervals
301
7.2.3.1
Pairwise-moving block bootstrap
301
7.2.3.2
Pairwise-autoregressive bootstrap
302
7.3
Monte Carlo experiments
302
Contents xix
7.4
7.5
7.6
7.7
8.
Re
8.1
Example: Elbe runoff variations
309
Unequal timescales
311
7.5.1
Binned correlation
312
7.5.2
Synchrony correlation
314
7.5.3
Monte Carlo experiments
316
7.5.3.1
Optimal estimation
321
7.5.4
Example: Vostok ice core records
322
Background material
323
Technical issues
338
ression II
339
Linear regression
340
8.1.1
Ordinary least-squares estimation
340
8.1.1.1
Bias correction
341
8.1.1.2
Prior knowledge about standard
deviations
341
8.1.2
Weighted least-squares for both variables
estimation
343
8.1.2.1
Prior knowledge about standard
deviation ratio
343
8.1.2.2
Geometric interpretation
344
8.1.3
Wald-Bartlett procedure
345
8.2
Bootstrap confidence intervals
346
8.2.1
Simulating incomplete prior knowledge
348
8.3
Monte Carlo experiments
350
8.3.1
Easy setting
350
8.3.2
Realistic setting: incomplete prior knowledge
353
8.3.3
Dependence on accuracy of prior knowledge
355
8.3.4
Mis-specified prior knowledge
357
8.4
Example: climate sensitivity
359
8.5
Prediction
362
8.5.1
Example: calibration of a proxy variable
364
8.6
Lagged regression
367
8.6.1
Example: CO2 and temperature variations in the
Pleistocene
368
8.7
Background material
373
8.8
Technical issues
379
xx Contents
Part IV
Outlook
9.
Future Directions
383
9.1
Timescale modelling
383
9.2
Novel estimation problems
384
9.3
Higher dimensions
385
9.4
Climate models
385
9.4.1
Fitting climate models to observations
387
9.4.2
Forecasting with climate models
388
9.4.3
Design of the cost function
389
9.4.4
Climate model bias
390
9.5
Optimal estimation
391
References
397
Subject Index
455
Author Index
467 |
| any_adam_object | 1 |
| author | Mudelsee, Manfred |
| author_facet | Mudelsee, Manfred |
| author_role | aut |
| author_sort | Mudelsee, Manfred |
| author_variant | m m mm |
| building | Verbundindex |
| bvnumber | BV036508012 |
| classification_rvk | QT 800 RB 10103 RB 10429 UT 6200 |
| ctrlnum | (OCoLC)705595742 (DE-599)DNB1001977408 |
| discipline | Physik Geologie / Paläontologie Wirtschaftswissenschaften Geographie |
| format | Book |
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| id | DE-604.BV036508012 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T10:39:33Z |
| institution | BVB |
| isbn | 9789048194810 9789048194827 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020430285 |
| oclc_num | 705595742 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-20 DE-384 DE-19 DE-BY-UBM |
| owner_facet | DE-355 DE-BY-UBR DE-20 DE-384 DE-19 DE-BY-UBM |
| physical | XXXIV, 474 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Springer |
| record_format | marc |
| series | Atmospheric and oceanographic sciences library |
| series2 | Atmospheric and oceanographic sciences library |
| spelling | Mudelsee, Manfred Verfasser aut Climate time series analysis classical statistical and bootstrap methods Manfred Mudelsee Dordrecht [u.a.] Springer 2010 XXXIV, 474 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Atmospheric and oceanographic sciences library 42 Klimatologie (DE-588)4031178-8 gnd rswk-swf Bootstrap-Statistik (DE-588)4139168-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Klimaanalyse (DE-588)4466593-3 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Klimaanalyse (DE-588)4466593-3 s Bootstrap-Statistik (DE-588)4139168-8 s 1\p DE-604 Klimatologie (DE-588)4031178-8 s Zeitreihenanalyse (DE-588)4067486-1 s 2\p DE-604 Datenanalyse (DE-588)4123037-1 s 3\p DE-604 Atmospheric and oceanographic sciences library 42 (DE-604)BV010783916 42 text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3463728&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020430285&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Mudelsee, Manfred Climate time series analysis classical statistical and bootstrap methods Atmospheric and oceanographic sciences library Klimatologie (DE-588)4031178-8 gnd Bootstrap-Statistik (DE-588)4139168-8 gnd Datenanalyse (DE-588)4123037-1 gnd Klimaanalyse (DE-588)4466593-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4031178-8 (DE-588)4139168-8 (DE-588)4123037-1 (DE-588)4466593-3 (DE-588)4067486-1 |
| title | Climate time series analysis classical statistical and bootstrap methods |
| title_auth | Climate time series analysis classical statistical and bootstrap methods |
| title_exact_search | Climate time series analysis classical statistical and bootstrap methods |
| title_full | Climate time series analysis classical statistical and bootstrap methods Manfred Mudelsee |
| title_fullStr | Climate time series analysis classical statistical and bootstrap methods Manfred Mudelsee |
| title_full_unstemmed | Climate time series analysis classical statistical and bootstrap methods Manfred Mudelsee |
| title_short | Climate time series analysis |
| title_sort | climate time series analysis classical statistical and bootstrap methods |
| title_sub | classical statistical and bootstrap methods |
| topic | Klimatologie (DE-588)4031178-8 gnd Bootstrap-Statistik (DE-588)4139168-8 gnd Datenanalyse (DE-588)4123037-1 gnd Klimaanalyse (DE-588)4466593-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Klimatologie Bootstrap-Statistik Datenanalyse Klimaanalyse Zeitreihenanalyse |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3463728&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020430285&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV010783916 |
| work_keys_str_mv | AT mudelseemanfred climatetimeseriesanalysisclassicalstatisticalandbootstrapmethods |