Statistical methods in the atmospheric sciences:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier
2011
|
| Ausgabe: | 3. ed. |
| Schriftenreihe: | International geophysics series
100 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XIX, 676 S. graph. Darst. |
| ISBN: | 9780123850225 |
Internformat
MARC
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| 100 | 1 | |a Wilks, Daniel S. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Statistical methods in the atmospheric sciences |c Daniel S. Wilks |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2011 | |
| 300 | |a XIX, 676 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a International geophysics series |v 100 | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Atmospheric physics |x Statistical methods | |
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Datensatz im Suchindex
| _version_ | 1804148584398979072 |
|---|---|
| adam_text | Contents
Preface
v
Part I
Preliminaries
1
1.
Introduction
3
1.1.
What Is Statistics?
3
1.2.
Descriptive and Inferential Statistics
3
1.3.
Uncertainty about the Atmosphere
4
2.
Review of Probability
7
2.1.
Background
7
2.2.
The Elements of Probability
7
2.2.1.
Events
7
2.2.2.
The Sample Space
8
2.2.3.
The Axioms of Probability
9
2.3.
The Meaning of Probability
9
2.3.1.
Frequency Interpretation
9
2.3.2.
Bayesian (Subjective) Interpretation
10
2.4.
Some Properties of Probability
10
2.4.1.
Domain, Subsets, Complements, and Unions
11
2.4.2.
DeMorgan s Laws
12
2.4.3.
Conditional Probability
13
2.4.4.
Independence
14
2.4.5.
Law of Total Probability
16
2.4.6.
Bayes
Theorem
17
2.5.
Exercises
18
Part II
Univariate Statistics
21
3.
Empirical Distributions and Exploratory Data Analysis
23
3.1.
Background
23
3.1.1.
Robustness and Resistance
23
3.1.2.
Quantiles
24
3.2.
Numerical Summary Measures
25
3.2.1.
Location
26
3.2.2.
Spread
26
3.2.3.
Symmetry
27
XI
Contents
3.3.
Graphical Summary Devices
28
3.3.1.
Stem-and-Leaf Display
28
3.3.2.
Boxplots
29
3.3.3.
Schematic Plots
31
3.3.4.
Other Boxplot Variants
33
3.3.5.
Histograms
33
3.3.6.
Kernel Density Smoothing
34
3.3.7.
Cumulative Frequency Distributions
39
3.4.
Reexpression
42
3.4.1.
Power Transformations
42
3.4.2.
Standardized Anomalies
46
3.5.
Exploratory Techniques for Paired Data
49
3.5.1.
Scatterplots
50
3.5.2.
Pearson (Ordinary) Correlation
50
3.5.3.
Spearman Rank Correlation and Kendall s
τ
55
3.5.4.
Serial Correlation
57
3.5.5.
Autocorrelation Function
59
3.6.
Exploratory Techniques for Higher-Dimensional Data
60
3.6.1.
The Star Plot
60
3.6.2.
The Glyph Scatterplot
61
3.6.3.
The Rotating Scatterplot
63
3.6.4.
The Correlation Matrix
63
3.6.5.
The Scatterplot Matrix
66
3.6.6.
Correlation Maps
67
3.7.
Exercises
70
4.
Parametric Probability Distributions
71
4.1.
Background
71
4.1.1.
Parametric versus Empirical Distributions
71
4.1.2.
What Is a Parametric Distribution?
72
4.1.3.
Parameters versus Statistics
72
4.1.4.
Discrete versus Continuous Distributions
72
4.2.
Discrete Distributions
73
4.2.1.
Binomial Distribution
73
4.2.2.
Geometric Distribution
76
4.2.3.
Negative Binomial Distribution
77
4.2.4.
Poisson
Distribution
80
4.3.
Statistical Expectations
82
4.3.1.
Expected Value of a Random Variable
82
4.3.2.
Expected Value of a Function of a Random Variable
83
4.4.
Continuous Distributions
85
4.4.1.
Distribution Functions and Expected Values
85
4.4.2.
Gaussian Distributions
87
4.4.3.
Gamma Distributions
95
4.4.4.
Beta Distributions
103
4.4.5.
Extreme-Value Distributions
105
4.4.6.
Mixture Distributions
110
Contents { xiii
4.5. Qualitative
Assessments of the Goodness of Fit
112
4.5.1.
Superposition of a Fitted Parametric Distribution and Data Histogram
113
4.5.2.
Quanti
le-Quanti
le
(Q-Q) Plots
115
4.6.
Parameter Fitting Using Maximum Likelihood
116
4.6.1.
The Likelihood Function
116
4.6.2.
The Newton-Raphson Method
118
4.6.3.
The EM Algorithm
119
4.6.4.
Sampling Distribution of Maximum-Likelihood Estimates
122
4.7.
Statistical Simulation
122
4.7.1.
Uniform Random-Number Generators
123
4.7.2.
Nonuniform
Random-Number Generation by Inversion
125
4.7.3.
Nonuniform
Random-Number Generation by Rejection
126
4.7.4.
Box-Muller Method for Gaussian Random-Number Generation
128
4.7.5.
Simulating from Mixture Distributions and Kernel Density Estimates
128
4.8.
Exercises
130
5.
Frequentisi
Statistical Inference
133
5.1.
Background
133
5.1.1.
Parametric versus Nonparametric Inference
133
5.1.2.
The Sampling Distribution
134
5.1.3.
The Elements of Any Hypothesis Test
134
5.1.4.
Test Levels and
ρ
Values
135
5.1.5.
Error Types and the Power of a Test
135
5.1.6.
One-Sided versus Two-Sided Tests
136
5.1.7.
Confidence Intervals: Inverting Hypothesis Tests
137
5.2.
Some Commonly Encountered Parametric Tests
141
5.2.1.
One-Sample
f
Test
141
5.2.2.
Tests for Differences of Mean under Independence
142
5.2.3.
Tests for Differences of Mean for Paired Samples
144
5.2.4.
Tests for Differences of Mean under Serial Dependence
145
5.2.5.
Goodness-of-Fit Tests
149
5.2.6.
Likelihood Ratio Tests
156
5.3.
Nonparametric Tests
158
5.3.1.
Classical Nonparametric Tests for Location
159
5.3.2.
Mann-Kendall Trend Test
166
5.3.3.
Introduction to Resampling Tests
168
5.3.4.
Permutation Tests
169
5.3.5.
The Bootstrap
172
5.4.
Multiplicity and Field Significance
178
5.4.1.
The Multiplicity Problem for Independent Tests
178
5.4.2.
Field Significance and the False Discovery Rate
180
5.4.3.
Field Significance and Spatial Correlation
181
5.5.
Exercises
185
6.
Bayesian Inference
187
6.1.
Background
187
6.2.
The Structure of Bayesian Inference
188
v^J
Contents
6.2.1.
Bayes
Theorem
for Continuous
Variables 188
6.2.2.
Inference and the Posterior Distribution
191
6.2.3.
The Role of the Prior Distribution
192
6.2.4.
The Predictive Distribution
194
6.3.
Conjugate Distributions
194
6.3.1.
Definition of Conjugate Distributions
194
6.3.2.
Binomial Data-Generating Process
195
6.3.3.
Poisson
Data-Generating Process
199
6.3.4.
Gaussian Data-Generating Process
203
6.4.
Dealing with Difficult Integrals
206
6.4.1.
Markov Chain Monte Carlo (MCMC) Methods
206
6.4.2.
The Metropolis-Hastings Algorithm
207
6.4.3.
The Gibbs Sampler
210
6.5.
Exercises
213
7.
Statistical Forecasting
215
7.1.
Background
215
7.2.
Linear Regression
215
7.2.1.
Simple Linear Regression
216
7.2.2.
Distribution of the Residuals
218
7.2.3.
The Analysis of Variance Table
220
7.2.4.
Goodness-of-Fit Measures
221
7.2.5.
Sampling Distributions of the Regression Coefficients
223
7.2.6.
Examining Residuals
225
7.2.7.
Prediction Intervals
230
7.2.8.
Multiple Linear Regression
233
7.2.9.
Derived Predictor Variables in Multiple Regression
233
7.3.
Nonlinear Regression
237
7.3.1.
Generalized Linear Models
237
7.3.2.
Logistic Regression
238
7.3.3.
Poisson
Regression
242
7.4.
Predictor Selection
244
7.4.1.
Why Is Careful Predictor Selection Important?
244
7.4.2.
Screening Predictors
247
7.4.3.
Stopping Rules
249
7.4.4.
Cross Validation
252
7.5.
Objective Forecasts Using Traditional Statistical Methods
255
7.5.1.
Classical Statistical Forecasting
255
7.5.2.
Perfect Prog and
MOS
257
7.5.3.
Operational
MOS
Forecasts
264
7.6.
Ensemble Forecasting
267
7.6.1.
Probabilistic Field Forecasts
267
7.6.2.
Stochastic Dynamical Systems in Phase Space
267
7.6.3.
Ensemble Forecasts
270
7.6.4.
Choosing Initial Ensemble Members
271
7.6.5.
Ensemble Average and Ensemble Dispersion
273
7.6.6.
Graphical Display of Ensemble Forecast Information
275
7.6.7.
Effects of Model Errors
282
Contents ( xv
7.7. Ensemble
MOS
284
7.7.1.
Why
Ensembles
Need Postprocessing
284
7.7.2.
Regression Methods
286
7.7.3.
Kernel Density (Ensemble Dressing ) Methods
290
7.8.
Subjective Probability Forecasts
292
7.8.1.
The Nature of Subjective Forecasts
292
7.8.2.
The Subjective Distribution
293
7.8.3.
Central Credible Interval Forecasts
294
7.8.4.
Assessing Discrete Probabilities
296
7.8.5.
Assessing Continuous Distributions
297
7.9.
Exercises
298
8.
Forecast Verification
301
8.1.
Background
301
8.1.1.
Purposes of Forecast Verification
301
8.1.2.
The joint Distribution of Forecasts and Observations
302
8.1.3.
Scalar Attributes of Forecast Performance
303
8.1.4.
Forecast Skill
305
8.2.
Nonprobabilistic Forecasts for Discrete Predictands
306
8.2.1.
The
2
χ
2
Contingency Table
306
8.2.2.
Scalar Attributes of the
2x2
Contingency Table
308
8.2.3.
Skill Scores for
2
χ
2
Contingency Tables
311
8.2.4.
Which Score?
315
8.2.5.
Conversion of Probabilistic to Nonprobabilistic Forecasts
316
8.2.6.
Extensions for Multicategory Discrete Predictands
318
8.3.
Nonprobabilistic Forecasts for Continuous Predictands
323
8.3.1.
Conditional Quantile Plots
324
8.3.2.
Scalar Accuracy Measures
325
8.3.3.
Skill Scores
327
8.4.
Probability Forecasts for Discrete Predictands
329
8.4.1.
The Joint Distribution for Dichotomous Events
329
8.4.2.
The Brier Score
331
8.4.3.
Algebraic Decomposition of the Brier Score
332
8.4.4.
The Reliability Diagram
334
8.4.5.
The Discrimination Diagram
340
8.4.6.
The Logarithmic, or Ignorance Score
341
8.4.7.
The ROC Diagram
342
8.4.8.
Hedging, and Strictly Proper Scoring Rules
346
8.4.9.
Probability Forecasts for Multiple-Category Events
348
8.5.
Probability Forecasts for Continuous Predictands
351
8.5.1.
Full Continuous Forecast Probability Distributions
351
8.5.2.
Central Credible Interval Forecasts
354
8.6.
Nonprobabilistic Forecasts for Fields
355
8.6.1.
General Considerations for Field Forecasts
355
8.6.2.
The S1 Score
357
8.6.3.
Mean Squared Error
359
8.6.4.
Anomaly Correlation
364
8.6.5.
Field Verification Based on Spatial Structure
367
Contents
8.7.
Verification of Ensemble Forecasts
369
8.7.1.
Characteristics of a Good Ensemble Forecast
369
8.7.2.
The Verification Rank Histogram
371
8.7.3.
Minimum Spanning Tree (MST) Histogram
375
8.7.4.
Shadowing, and Bounding Boxes
376
8.8.
Verification Based on Economic Value
377
8.8.1.
Optimal Decision Making and the Cost/Loss Ratio Problem
377
8.8.2.
The Value Score
379
8.8.3.
Connections with Other Verification Approaches
381
8.9.
Verification When the Observation is Uncertain
382
8.10.
Sampling and Inference for Verification Statistics
383
8.10.1.
Sampling Characteristics of Contingency Table Statistics
383
8.10.2.
ROC Diagram Sampling Characteristics
386
8.10.3.
Brier Score and Brier Skill Score Inference
388
8.10.4.
Reliability Diagram Sampling Characteristics
389
8.10.5.
Resampling Verification Statistics
390
8.11.
Exercises
391
9.
Time Series
395
9.1.
Background
395
9.1.1.
Stationarity
395
9.1.2.
Time-Series Models
396
9.1.3.
Time-Domain versus Frequency-Domain Approaches
396
9.2.
Time Domain—I. Discrete Data
397
9.2.1.
Markov Chains
397
9.2.2.
Two-State, First-Order Markov Chains
398
9.2.3.
Test for Independence versus First-Order Serial Dependence
402
9.2.4.
Some Applications of Two-State Markov Chains
404
9.2.5.
Multiple-State Markov Chains
406
9.2.6.
Higher-Order Markov Chains
407
9.2.7.
Deciding among Alternative Orders of Markov Chains
408
9.3.
Time Domain—II. Continuous Data
410
9.3.1.
First-Order
Autoregression 410
9.3.2.
Higher-Order
Autoregressions 414
9.3.3.
The AR(2) Model
415
9.3.4.
Order Selection Criteria
419
9.3.5.
The Variance of a Time Average
421
9.3.6.
Autoregressive-Moving Average Models
423
9.3.7.
Simulation and Forecasting with Continuous Time-Domain Models
424
9.4.
Frequency Domain —I. Harmonic Analysis
428
9.4.1.
Cosine and Sine Functions
428
9.4.2.
Representing a Simple Time Series with a Harmonic Function
429
9.4.3.
Estimation of the Amplitude and Phase of a Single Harmonic
432
9.4.4.
Higher Harmonics
435
9.5.
Frequency Domain—II. Spectral Analysis
438
9.5.1.
The Harmonic Functions as Uncorrelated Regression Predictors
438
9.5.2.
The
Periodogram,
or Fourier Line Spectrum
440
Contents Q xvi
9.5.3. Computing
Spectra
444
9.5.4.
Aliasing
445
9.5.5.
The Spectra of
Autoregressive
Models
447
9.5.6.
Sampling Properties of Spectral Estimates
450
9.6.
Exercises
455
Part III
Multivariate Statistics
457
10.
Matrix Algebra and Random Matrices
459
10.1.
Background to Multivariate Statistics
459
10.1.1.
Contrasts between Multivariate and Univariate Statistics
459
10.1.2.
Organization of Data and Basic Notation
459
10.1.3.
Multivariate Extensions of Common Univariate Statistics
460
10.2.
Multivariate Distance
461
10.2.1.
Euclidean Distance
462
10.2.2.
Mahalanobis (Statistical) Distance
463
10.3.
Matrix Algebra Review
464
10.3.1.
Vectors
464
10.3.2.
Matrices
467
10.3.3.
Eigenvalues and Eigenvectors of a Square Matrix
476
10.3.4.
Square Roots of a Symmetric Matrix
479
10.3.5.
Singular-Value Decomposition
(SVD)
481
10.4.
Random Vectors and Matrices
482
10.4.1.
Expectations and Other Extensions of Univariate Concepts
482
10.4.2.
Partitioning Vectors and Matrices
483
10.4.3.
Linear Combinations
485
10.4.4.
Mahalanobis Distance, Revisited
487
10.5.
Exercises
489
11.
The Multivariate Normal (MVN) Distribution
491
11.1.
Definition of the MVN
491
11.2.
Four Handy Properties of the MVN
493
11.3.
Assessing Multinormality
496
11.4.
Simulation from the Multivariate Normal Distribution
499
11.4.1.
Simulating Independent MVN
Variâtes
499
11.4.2.
Simulating Multivariate Time Series
500
11.5.
Inferences about
a
Multinormal
Mean Vector
504
11.5.1.
Multivariate Central Limit Theorem
504
11.5.2.
Hotelling s T2
505
11.5.3.
Simultaneous Confidence Statements
511
11.5.4.
Interpretation of Multivariate Statistical Significance
515
11.6.
Exercises
517
vu
j j
Contents
12.
Principal
Component
(EOF) Analysis
519
12.1.
Basics of Principal Component Analysis
519
12.1.1.
Definition of PCA
519
12.1.2.
PCA Based on the Covariance Matrix versus the Correlation Matrix
525
1 2.1.3.
The Varied Terminology of PCA
527
12.1.4.
Scaling Conventions in PCA
528
12.1.5.
Connections to the Multivariate Normal Distribution
530
12.2.
Application of PCA to Geophysical Fields
531
12.2.1.
PCA for a Single Field
531
12.2.2.
Simultaneous PCA for Multiple Fields
533
12.2.3.
Scaling Considerations and Equalization of Variance
536
12.2.4.
Domain Size Effects: Buell Patterns
536
12.3.
Truncation of the Principal Components
538
12.3.1.
Why Truncate the Principal Components?
538
12.3.2.
Subjective Truncation Criteria
539
12.3.3.
Rules Based on the Size of the Last Retained Eigenvalue
539
12.3.4.
Rules Based on Hypothesis-Testing Ideas
541
12.3.5.
Rules Based on Structure in the Retained Principal Components
542
12.4.
Sampling Properties of the Eigenvalues and Eigenvectors
542
12.4.1.
Asymptotic Sampling Results for Multivariate Normal Data
542
12.4.2.
Effective
Multiplets
544
1 2.4.3.
The North
et al.
Rule of Thumb
545
12.4.4.
Bootstrap Approximations to the Sampling Distributions
547
12.5.
Rotation of the Eigenvectors
547
12.5.1.
Why Rotate the Eigenvectors?
547
12.5.2.
Rotation Mechanics
548
12.5.3.
Sensitivity of Orthogonal Rotation to Initial Eigenvector Scaling
551
12.6.
Computational Considerations
554
12.6.1.
Direct Extraction of Eigenvalues and Eigenvectors from
[5] 554
12.6.2.
PCA via
SVD
555
12.7.
Some Additional Uses of PCA
555
12.7.1.
Singular Spectrum Analysis (SSA): Time-Series PCA
555
12.7.2.
Principal-Component Regression
559
12.7.3.
The Biplot
560
12.8.
Exercises
562
13.
Canonical Correlation Analysis (CCA)
563
13.1.
Basics of CCA
563
13.1.1.
Overview
563
13.1.2.
Canonical
Variâtes,
Canonical Vectors, and Canonical Correlations
564
1 3.1.3.
Some Additional Properties of CCA
565
13.2.
CCA Applied to Fields
571
13.2.1.
Translating Canonical Vectors to Maps
571
13.2.2.
Combining CCA with PCA
572
13.2.3.
Forecasting with CCA
572
13.3.
Computational Considerations
576
13.3.1.
Calculating CCA through Direct Eigendecomposition
576
13.3.2.
CCA via
SVD
577
Contents
Ç
xix
13.4. Maximum Covariance
Analysis (MCA)
580
13.5.
Exercises
582
14.
Discrimination and Classification
583
14.1.
Discrimination versus Classification
583
14.2.
Separating Two Populations
583
14.2.1.
Equal Covariance Structure: Fisher s Linear Discriminant
583
14.2.2.
Fisher s Linear Discriminant for Multivariate Normal Data
588
14.2.3.
Minimizing Expected Cost of Misclassification
589
14.2.4.
Unequal Covariances: Quadratic Discrimination
591
14.3.
Multiple Discriminant Analysis
(MDA)
592
14.3.1.
Fisher s Procedure for More Than Two Croups
592
14.3.2.
Minimizing Expected Cost of Misclassification
595
14.3.3.
Probabilistic Classification
596
14.4.
Forecasting with Discriminant Analysis
597
14.5.
Alternatives to Classical Discriminant Analysis
599
14.5.1.
Discrimination and Classification Using Logistic Regression
599
14.5.2.
Discrimination and Classification Using Kernel Density Estimates
600
14.6.
Exercises
601
15.
Cluster Analysis
603
15.1.
Background
603
15.1.1.
Cluster Analysis versus Discriminant Analysis
603
15.1.2.
Distance Measures and the Distance Matrix
603
15.2.
Hierarchical Clustering
604
15.2.1.
Agglomerative Methods Using the Distance Matrix
604
15.2.2.
Ward s Minimum Variance Method
606
15.2.3.
The Dendrogram, or Tree Diagram
607
15.2.4.
How Many Clusters?
607
15.2.5.
Divisive Methods
612
15.3.
Nonhierarchical Clustering
614
15.3.1.
The K-Means Method
614
15.3.2.
Nucleated Agglomerative Clustering
614
15.3.3.
Clustering Using Mixture Distributions
615
15.4.
Exercises
615
Appendix A Example Data Sets
617
Appendix
В
Probability Tables
619
Appendix
С
Answers to Exercises
627
References
635
Index
661
|
| any_adam_object | 1 |
| author | Wilks, Daniel S. |
| author_facet | Wilks, Daniel S. |
| author_role | aut |
| author_sort | Wilks, Daniel S. |
| author_variant | d s w ds dsw |
| building | Verbundindex |
| bvnumber | BV039709990 |
| callnumber-first | Q - Science |
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| callnumber-search | QC874.5 |
| callnumber-sort | QC 3874.5 |
| callnumber-subject | QC - Physics |
| classification_rvk | RB 10426 WC 5500 |
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| ctrlnum | (OCoLC)762279039 (DE-599)BVBBV039709990 |
| dewey-full | 551.5 |
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| dewey-raw | 551.5 |
| dewey-search | 551.5 |
| dewey-sort | 3551.5 |
| dewey-tens | 550 - Earth sciences |
| discipline | Geowissenschaften Geologie / Paläontologie Physik Biologie Geographie |
| edition | 3. ed. |
| format | Book |
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| genre_facet | Statistik |
| id | DE-604.BV039709990 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:09:27Z |
| institution | BVB |
| isbn | 9780123850225 |
| language | English |
| lccn | 2011007894 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024558363 |
| oclc_num | 762279039 |
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| owner_facet | DE-703 DE-384 DE-91 DE-BY-TUM DE-188 DE-19 DE-BY-UBM DE-29 |
| physical | XIX, 676 S. graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Elsevier |
| record_format | marc |
| series | International geophysics series |
| series2 | International geophysics series |
| spelling | Wilks, Daniel S. Verfasser aut Statistical methods in the atmospheric sciences Daniel S. Wilks 3. ed. Amsterdam [u.a.] Elsevier 2011 XIX, 676 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier International geophysics series 100 Includes bibliographical references and index Atmospheric physics Statistical methods Atmosphäre (DE-588)4003397-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Anwendung (DE-588)4196864-5 gnd rswk-swf Physik (DE-588)4045956-1 gnd rswk-swf Meteorologie (DE-588)4038953-4 gnd rswk-swf 1\p (DE-588)4056995-0 Statistik gnd-content Atmosphäre (DE-588)4003397-1 s Physik (DE-588)4045956-1 s Statistik (DE-588)4056995-0 s Anwendung (DE-588)4196864-5 s 2\p DE-604 Meteorologie (DE-588)4038953-4 s 3\p DE-604 International geophysics series 100 (DE-604)BV000005107 100 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024558363&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 | Wilks, Daniel S. Statistical methods in the atmospheric sciences International geophysics series Atmospheric physics Statistical methods Atmosphäre (DE-588)4003397-1 gnd Statistik (DE-588)4056995-0 gnd Anwendung (DE-588)4196864-5 gnd Physik (DE-588)4045956-1 gnd Meteorologie (DE-588)4038953-4 gnd |
| subject_GND | (DE-588)4003397-1 (DE-588)4056995-0 (DE-588)4196864-5 (DE-588)4045956-1 (DE-588)4038953-4 |
| title | Statistical methods in the atmospheric sciences |
| title_auth | Statistical methods in the atmospheric sciences |
| title_exact_search | Statistical methods in the atmospheric sciences |
| title_full | Statistical methods in the atmospheric sciences Daniel S. Wilks |
| title_fullStr | Statistical methods in the atmospheric sciences Daniel S. Wilks |
| title_full_unstemmed | Statistical methods in the atmospheric sciences Daniel S. Wilks |
| title_short | Statistical methods in the atmospheric sciences |
| title_sort | statistical methods in the atmospheric sciences |
| topic | Atmospheric physics Statistical methods Atmosphäre (DE-588)4003397-1 gnd Statistik (DE-588)4056995-0 gnd Anwendung (DE-588)4196864-5 gnd Physik (DE-588)4045956-1 gnd Meteorologie (DE-588)4038953-4 gnd |
| topic_facet | Atmospheric physics Statistical methods Atmosphäre Statistik Anwendung Physik Meteorologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024558363&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000005107 |
| work_keys_str_mv | AT wilksdaniels statisticalmethodsintheatmosphericsciences |