Models for ecological data: an introduction
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Princeton [u.a.]
Princeton Univ. Press
2007
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIII, 617 S. graph. Darst. |
| ISBN: | 9780691121789 0691121788 |
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| adam_text | Contents
Preface ix
Part I. Introduction i
1. Models in Context 3
1.1 Complexity and Obscurity in Nature and in Models 3
1.2 Making the Connections: Data, Inference, and Decision 5
1.3 Two Elements ot Models: Known and Unknown 13
1.4 Learning with Models: Hypotheses and Quantification 19
1.5 Estimation versus Forward Simulation 23
1.6 Statistical Pragmatism 24
2. Model Elements: Application to Population Growth n
2.1 A Model and Data Example 27
2.2 Model State and Time 30
2.3 Stochasticity for the Unknown 42
2.4 Additional Background on Process Models 44
Part II. Elements of Inference
45
3. Point Estimation: Maximum Likelihood and the Method of Moments
3.1 Introduction 47
3.2 Likelihood 47
3.3 A Binomial Model 53
3.4 Combining the Binomial and Exponential 54
3.5 Maximum Likelihood Estimates for the Normal Distribution 56
3.6 Population Growth 57
3.7 Application: Fecundity 60
.3.8 Survival Analysis Using Maximum Likelihood 62
3.9 Design Matrixes 68
3.10 Numerical Methods tor MLF. 71
CONTENTS
3.11 Moment Matching 71
3.12 Common Sampling Distributions and Dispersion 74
3.13 Assumptions and Next Steps 76
4. Elements of the Bayesian Approach 77
4.1 The Bayesian Approach 78
4.2 The Normal Distribution 84
4.3 Subjective Probability and the Role of the Prior 91
5. Confidence Envelopes and Prediction Intervals 93
5.1 Classical Interval Estimation 95
5.2 Bayesian Credible Intervals 115
5.3 Likelihood Profile for Multiple Parameters 120
5.4 Confidence Intervals for Several Parameters: Linear Regression 122
5.5 Which Confidence Envelope to Use 130
5.6 Predictive Intervals 133
5.7 Uncertainty and Variability 141
5.8 When Is It Bayesian? 142
6. Model Assessment and Selection 143
6.1 Using Statistics to Evaluate Models 143
6.2 The Role of Hypothesis Tests 144
6.3 Nested Models 144
6.4 Additional Considerations for Classical Model Selection 151
6.5 Bayesian Model Assessment 154
6.6 Additional Thoughts on Bayesian Model Assessment 159
Part III. Larger Models
161
7. Computational Bayes: Introduction to Tools Simulation m
7.1 Simulation to Obtain the Posterior 163
7.2 Some Basic Simulation Techniques 164
7.3 Markov Chain Monte Carlo Simulation 173
7.4 Application: Bayesian Analysis for Regression 189
7.5 Using MCMC 202
7.6 Computation for Bayesian Model Selection 205
7.7 Priors on the Response 209
7.8 The Basics Are Now Behind Us 212
8. A Closer Look at Hierarchical Structures 213
8.1 Hierarchical Models for Context 213
8.2 Mixed and Generalized Linear Models 216
8.3 Application: Growth Responses to CO2 230
8.4 Thinking Conditionally 235
8.5 Two Applications to Trees 241
CONTENTS • vii
8.6 Noninformative Priors in Hierarchical Settings 249
8.7 From Simple Models to Graphs 249
Part IV. More Advance Methods
251
9. Time
9.1 Why Is Time Important? 253
9.2 Time Series Terminology 254
9.3 Descriptive Elements of Time Series Models 255
9.4 The Frequency Domain 264
9.5 Application: Detecting Density Dependence in Population
Time Series 264
9.6 Bayesian State Space Models 272
9.7 Application: Black Noddy on Heron Island 282
9.8 Nonlinear State Space Models 289
9.9 Lags 297
9.10 Regime Change 298
9.11 Constraints on Time Series Data 300
9.12 Additional Sources of Variablity 301
9.13 Alternatives to the Gibbs Sampler 302
9.14 More on Longitudinal Data Structures 302
9.15 Intervention and Treatment Effects 309
9.16 Capture-Recapture Studies 318
9.17 Structured Models as Matrixes 329
9.18 Structure as Systems of Difference Equations 336
9.19 Time Series, Population Regulation, and Stochasticity 347
10. Space-Time 353
10.1 A Deterministic Model for a Stochastic Spatial Process 354
10.2 Classical Inference on Population Movement 359
10.3 Island Biogeography and Metapopulations 378
10.4 Estimation of Passive Dispersal 388
10.5 A Bayesian Framework 397
10.6 Models for Explicit Space 401
10.7 Point-Referenced Data 403
10.8 Block-Referenced Data and Misalignment 412
10.9 Hierarchical Treatment of Space 415
10.10 Application: A Spatio-Temporal Model of
Population Spread 424
10.11 How to Handle Space 432
]. Some Concluding Perspectives 435
11.1 Models, Data, and Decision 435
11.2 The Promise of Graphical Models, Improved Algorithms, and
Faster Computers 437
viii • CONTENTS
11.3 Predictions and What to Do with Them 444
11.4 Some Remarks on Software 456
Appendix A Taylor Series 457
Appendix B Some Notes on Differential and Difference Equations 464
Appendix C Basic Matrix Algebra 486
Appendix D Probability Models 502
Appendix E Basic Life History Calculations 541
Appendix F Common Distributions 573
Appendix G Common Conjugate Likelihood-Prior Pairs 583
References 585
Index 615
|
| adam_txt |
Contents
Preface ix
Part I. Introduction i
1. Models in Context 3
1.1 Complexity and Obscurity in Nature and in Models 3
1.2 Making the Connections: Data, Inference, and Decision 5
1.3 Two Elements ot Models: Known and Unknown 13
1.4 Learning with Models: Hypotheses and Quantification 19
1.5 Estimation versus Forward Simulation 23
1.6 Statistical Pragmatism 24
2. Model Elements: Application to Population Growth n
2.1 A Model and Data Example 27
2.2 Model State and Time 30
2.3 Stochasticity for the Unknown 42
2.4 Additional Background on Process Models 44
Part II. Elements of Inference
45
3. Point Estimation: Maximum Likelihood and the Method of Moments
3.1 Introduction 47
3.2 Likelihood 47
3.3 A Binomial Model 53
3.4 Combining the Binomial and Exponential 54
3.5 Maximum Likelihood Estimates for the Normal Distribution 56
3.6 Population Growth 57
3.7 Application: Fecundity 60
.3.8 Survival Analysis Using Maximum Likelihood 62
3.9 Design Matrixes 68
3.10 Numerical Methods tor MLF. 71
CONTENTS
3.11 Moment Matching 71
3.12 Common Sampling Distributions and Dispersion 74
3.13 Assumptions and Next Steps 76
4. Elements of the Bayesian Approach 77
4.1 The Bayesian Approach 78
4.2 The Normal Distribution 84
4.3 Subjective Probability and the Role of the Prior 91
5. Confidence Envelopes and Prediction Intervals 93
5.1 Classical Interval Estimation 95
5.2 Bayesian Credible Intervals 115
5.3 Likelihood Profile for Multiple Parameters 120
5.4 Confidence Intervals for Several Parameters: Linear Regression 122
5.5 Which Confidence Envelope to Use 130
5.6 Predictive Intervals 133
5.7 Uncertainty and Variability 141
5.8 When Is It Bayesian? 142
6. Model Assessment and Selection 143
6.1 Using Statistics to Evaluate Models 143
6.2 The Role of Hypothesis Tests 144
6.3 Nested Models 144
6.4 Additional Considerations for Classical Model Selection 151
6.5 Bayesian Model Assessment 154
6.6 Additional Thoughts on Bayesian Model Assessment 159
Part III. Larger Models
161
7. Computational Bayes: Introduction to Tools Simulation m
7.1 Simulation to Obtain the Posterior 163
7.2 Some Basic Simulation Techniques 164
7.3 Markov Chain Monte Carlo Simulation 173
7.4 Application: Bayesian Analysis for Regression 189
7.5 Using MCMC ' 202
7.6 Computation for Bayesian Model Selection 205
7.7 Priors on the Response 209
7.8 The Basics Are Now Behind Us 212
8. A Closer Look at Hierarchical Structures 213
8.1 Hierarchical Models for Context 213
8.2 Mixed and Generalized Linear Models 216
8.3 Application: Growth Responses to CO2 230
8.4 Thinking Conditionally 235
8.5 Two Applications to Trees 241
CONTENTS • vii
8.6 Noninformative Priors in Hierarchical Settings 249
8.7 From Simple Models to Graphs 249
Part IV. More Advance Methods
251
9. Time
9.1 Why Is Time Important? 253
9.2 Time Series Terminology 254
9.3 Descriptive Elements of Time Series Models 255
9.4 The Frequency Domain 264
9.5 Application: Detecting Density Dependence in Population
Time Series 264
9.6 Bayesian State Space Models 272
9.7 Application: Black Noddy on Heron Island 282
9.8 Nonlinear State Space Models 289
9.9 Lags 297
9.10 Regime Change 298
9.11 Constraints on Time Series Data 300
9.12 Additional Sources of Variablity 301
9.13 Alternatives to the Gibbs Sampler 302
9.14 More on Longitudinal Data Structures 302
9.15 Intervention and Treatment Effects 309
9.16 Capture-Recapture Studies 318
9.17 Structured Models as Matrixes 329
9.18 Structure as Systems of Difference Equations 336
9.19 Time Series, Population Regulation, and Stochasticity 347
10. Space-Time 353
10.1 A Deterministic Model for a Stochastic Spatial Process 354
10.2 Classical Inference on Population Movement 359
10.3 Island Biogeography and Metapopulations 378
10.4 Estimation of Passive Dispersal 388
10.5 A Bayesian Framework 397
10.6 Models for Explicit Space 401
10.7 Point-Referenced Data 403
10.8 Block-Referenced Data and Misalignment 412
10.9 Hierarchical Treatment of Space 415
10.10 Application: A Spatio-Temporal Model of
Population Spread 424
10.11 How to Handle Space 432
\]. Some Concluding Perspectives 435
11.1 Models, Data, and Decision 435
11.2 The Promise of Graphical Models, Improved Algorithms, and
Faster Computers 437
viii • CONTENTS
11.3 Predictions and What to Do with Them 444
11.4 Some Remarks on Software 456
Appendix A Taylor Series 457
Appendix B Some Notes on Differential and Difference Equations 464
Appendix C Basic Matrix Algebra 486
Appendix D Probability Models 502
Appendix E Basic Life History Calculations 541
Appendix F Common Distributions 573
Appendix G Common Conjugate Likelihood-Prior Pairs 583
References 585
Index 615 |
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| index_date | 2024-07-02T17:45:48Z |
| indexdate | 2024-07-09T20:58:23Z |
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| isbn | 9780691121789 0691121788 |
| language | English |
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| spelling | Clark, James Samuel 1957- Verfasser (DE-588)1055796290 aut Models for ecological data an introduction James S. Clark Princeton [u.a.] Princeton Univ. Press 2007 XIII, 617 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Sciences de l'environnement - Modèles mathématiques Écologie - Modèles mathématiques Mathematisches Modell Ökologie Ecology Mathematical models Environmental sciences Mathematical models Ökosystem (DE-588)4043216-6 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Ökosystem (DE-588)4043216-6 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Ergänzung Clark, James Samuel Statistical computation for environmental sciences in R HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015681625&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Clark, James Samuel 1957- Models for ecological data an introduction Sciences de l'environnement - Modèles mathématiques Écologie - Modèles mathématiques Mathematisches Modell Ökologie Ecology Mathematical models Environmental sciences Mathematical models Ökosystem (DE-588)4043216-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4043216-6 (DE-588)4114528-8 |
| title | Models for ecological data an introduction |
| title_auth | Models for ecological data an introduction |
| title_exact_search | Models for ecological data an introduction |
| title_exact_search_txtP | Models for ecological data an introduction |
| title_full | Models for ecological data an introduction James S. Clark |
| title_fullStr | Models for ecological data an introduction James S. Clark |
| title_full_unstemmed | Models for ecological data an introduction James S. Clark |
| title_short | Models for ecological data |
| title_sort | models for ecological data an introduction |
| title_sub | an introduction |
| topic | Sciences de l'environnement - Modèles mathématiques Écologie - Modèles mathématiques Mathematisches Modell Ökologie Ecology Mathematical models Environmental sciences Mathematical models Ökosystem (DE-588)4043216-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Sciences de l'environnement - Modèles mathématiques Écologie - Modèles mathématiques Mathematisches Modell Ökologie Ecology Mathematical models Environmental sciences Mathematical models Ökosystem |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015681625&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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