Model based inference in the life sciences: a primer on evidence
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Springer Science + Business Media
2008
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016495753&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XXIV, 184 S. Ill., graph. Darst. 235 mm x 155 mm |
| ISBN: | 9780387740737 |
Internformat
MARC
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| 100 | 1 | |a Anderson, David Raymond |d 1942- |e Verfasser |0 (DE-588)122291727 |4 aut | |
| 245 | 1 | 0 | |a Model based inference in the life sciences |b a primer on evidence |c David R. Anderson |
| 264 | 1 | |a New York |b Springer Science + Business Media |c 2008 | |
| 300 | |a XXIV, 184 S. |b Ill., graph. Darst. |c 235 mm x 155 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Inférence (Logique) | |
| 650 | 4 | |a Sciences de la vie - Modèles mathématiques | |
| 650 | 4 | |a Biowissenschaften | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Inference | |
| 650 | 4 | |a Life sciences |x Mathematical models | |
| 650 | 0 | 7 | |a Mathematisches Modell |0 (DE-588)4114528-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistische Schlussweise |0 (DE-588)4182963-3 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1819313346306899968 |
|---|---|
| adam_text | Contents
Preface
..........................................................................................................
vii
About the Author
........................................................................................ xvii
Glossary
....................................................................................................... xix
1.
Introduction: Science Hypotheses and Science Philosophy
............. 1
1.1
Some Science Background
.................................................... 1
1.2
Multiple Working Hypotheses
............................................... 3
1.3
Bovine
ТВ
Transmission in Ferrets
....................................... 4
1.4
Approaches to Scientific Investigations
................................ 6
1.4.1
Experimental Studies
................................................. 7
1.4.2
Descriptive Studies
.................................................... 8
1.4.3
Confirmatory Studies
................................................. 8
1.5
Science Hypothesis Set Evolves
............................................ 10
1.6
Null Hypothesis Testing
........................................................ 11
1.7
Evidence and Inferences
........................................................ 12
1.8
Hardening of Portland Cement
.............................................. 13
1.9
What Does Science Try to Provide?
...................................... 14
1.10
Remarks
................................................................................. 15
1.11
Exercises
................................................................................ 17
2.
Data and Models
.................................................................................. 19
2.1
Data
.......................................................................................... 19
2.1.1
Hardening of Portland Cement Data
........................... 22
2.1.2
Bovine
ТВ
Transmission in Ferrets
............................. 23
2.1.3
What Constitutes a Data Set ?
.................................. 24
xiv Contents
2.2 Models..................................................................................... 25
2.2.1
Trae
Models (An Oxymoron)...................................... 27
2.2.2 The
Concept
of
Model Parameters.............................. 28
2.2.3 Parameter
Estimation
.................................................. 29
2.2.4
Principle of Parsimony
................................................
ЗО
2.2.5
Tapering Effect Sizes
................................................... 33
2.3
Casestudies
............................................................................. 33
2.3.1
Models of Hardening of Portland Cement Data
.......... 33
2.3.2
Models of Bovine
ТВ
Transmission in Ferrets
........... 35
2.4
Additional Examples of Modeling
.......................................... 36
2.4.1
Modeling Beak Lengths
.............................................. 37
2.4.2
Modeling Dose Response in Flour Beetles
................. 41
2.4.3
Modeling Enzyme Kinetics
......................................... 44
2.5
Data Dredging
......................................................................... 45
2.6
The Effect of a Flood on European
Dippers: Modeling Contrasts
.................................................. 46
2.6.1
Traditional Null Hypothesis Testing
........................... 46
2.6.2
Information-Theoretic Approach
................................ 47
2.7
Remarks
................................................................................... 48
2.8
Exercises
.................................................................................. 49
3.
Information Theory and Entropy
...................................................... 51
3.1
Kullback-Leibler Information
................................................. 52
3.2
Linking Information Theory to Statistical Theory
.................. 54
3.3
Akaike s Information Criterion
............................................... 55
3.3.1
The Bias Correction Term
........................................... 57
3.3.2
Why Multiply by
-2?.................................................. 57
3.3.3
Parsimony is Achieved as a by-Product
...................... 58
3.3.4
Simple vs. Complex Models
....................................... 59
3.3.5
AIC Scale
.................................................................... 60
3.4
A Second-Order Bias Correction: AICc
.................................. 60
3.5
Regression Analysis
................................................................ 61
3.6
Additional Important Points
.................................................... 62
3.6.1
Differences Among AICc Values
................................ 62
3.6.2
Nested vs. Nonnested Models
..................................... 63
3.6.3
Data and Response Variable Must Remain Fixed
....... 63
3.6.4
AICc is not a Test
..................................................... 64
3.6.5
Data Dredging Using AICc
......................................... 64
3.6.6
Keep all the Model Terms
........................................... 64
3.6.7
Missing Data
............................................................... 65
3.6.8
The Pretending Variable
.......................................... 65
3.7
Cement Hardening Data
.......................................................... 66
3.7.1
Interpreting AICc Values
............................................. 66
3.7.2
What if all the Models are Bad?
.................................. 67
3.7.3
Prediction from the Best Model
.................................. 68
Contents xv
3.8 Ranking
the
Models
of
Bovine
Tuberculosis in Ferrets
........ 69
3.9
Other Important Issues
.......................................................... 70
3.9.1
Takeuchi s Information Criterion
............................... 70
3.9.2
Problems When Evaluating Too Many
Candidate Models
...................................................... 71
3.9.3
The Parameter Count
К
and Parameters
that Cannot be Uniquely Estimated
........................... 71
3.9.4
Cross Validation and AICc
........................................ 72
3.9.5
Science Advances as the Hypothesis
Set Evolves
................................................................. 72
3.10
Summary
................................................................................ 73
3.11
Remarks
................................................................................. 74
3.12
Exercises
................................................................................ 80
4.
Quantifying the Evidence About Science Hypotheses
...................... 83
4.1
Δ
Values and Ranking
........................................................... 84
4.2
Model Likelihoods
................................................................. 86
4.3
Model Probabilities
............................................................... 87
4.4
Evidence Ratios
..................................................................... 89
4.5
Hardening of Portland Cement
.............................................. 91
4.6
Bovine Tuberculosis in Ferrets
.............................................. 93
4.7
Return to Flather s Models and R2
......................................... 94
4.8
The Effect of a Flood on European Dippers
.......................... 95
4.9
More about Evidence and Inference
...................................... 98
4.10
Summary
................................................................................ 100
4.11
Remarks
................................................................................. 101
4.12
Exercises
................................................................................ 103
5.
Multimodel Inference
.......................................................................... 105
5.1
Model Averaging
..................................................................... 106
5.1.1
Model Averaging for Prediction
.................................. 107
5.1.2
Model Averaging Parameter
Estimates Across Models
............................................ 108
5.2
Unconditional Variances
.......................................................... 110
5.2.1
Examples Using the Cement Hardening Data
............. 112
5.2.2
Averaging Detection Probability Parameters
in Occupancy Models
.................................................. 115
5.3
Relative Importance of Predictor Variables
............................. 118
5.3.1
Rationale for Ranking the Relative Importance
of Predictor Variables
.................................................. 119
5.3.2
An Example Using the Cement Hardening Data
........ 119
5.4
Confidence Sets on Models
..................................................... 121
5.5
Summary
.................................................................................. 122
5.6
Remarks
................................................................................... 122
5.7
Exercises
.................................................................................. 124
xvi Contents
6. Advanced
Topics..................................................................................
125
6.1 Overdispersed
Count Data
....................................................... 126
6.1.1
Lack of Independence.................................................
126
6.1.2 Parameter
Heterogeneity
............................................. 126
6.1.3
Estimation of a Variance
Inflation
Factor
.................... 127
6.1.4
Coping with Overdispersion in Count Data
................ 127
6.1.5
Overdispersion in Data on Elephant Seals
.................. 128
6.2
Model Selection Bias
............................................................... 129
6.2.1
Understanding the Issue
.............................................. 129
6.2.2
A Solution to the Problem of Model
Selection Bias
.............................................................. 130
6.3
Multivariate AICc
.................................................................... 133
6.4
Model Redundancy
.................................................................. 133
6.5
Model Selection in Random Effects Models
........................... 134
6.6
Use in Conflict Resolution
...................................................... 135
6.6.1
Analogy with the Flip of a Coin
.................................. 136
6.6.2
Conflict Resolution Protocol
....................................... 137
6.6.3
A Hypothetical Example: Hen Clam
Experiments
................................................................. 138
6.7
Remarks
................................................................................... 140
7.
Summary
............................................................................................... 141
7.1
The Science Question
.............................................................. 142
7.2
Collection of Relevant Data
.................................................... 143
7.3
Mathematical Models
.............................................................. 143
7.4
Data Analysis
........................................................................... 144
7.5
Information and Entropy
......................................................... 144
7.6
Quantitative Measures of Evidence
......................................... 144
7.7
Inferences
................................................................................ 145
7.8
Post Hoc Issues
........................................................................ 146
7.9
Final Comment
........................................................................ 146
Appendices
................................................................................................... 147
Appendix A: Likelihood Theory
...................................................... 147
Appendix B: Expected Values
.......................................................... 155
Appendix C: Null Hypothesis Testing
.............................................. 157
Appendix D: Bayesian Approaches
.................................................. 158
Appendix E: The Bayesian Information Criterion
........................... 160
Appendix F: Common Misuses and Misinterpretations
.................. 162
References
.................................................................................................... 167
Index
............................................................................................................. 181
|
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| id | DE-604.BV023311491 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:13:14Z |
| institution | BVB |
| isbn | 9780387740737 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016495753 |
| oclc_num | 195612532 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-11 DE-83 DE-Grf2 DE-20 |
| owner_facet | DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-11 DE-83 DE-Grf2 DE-20 |
| physical | XXIV, 184 S. Ill., graph. Darst. 235 mm x 155 mm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer Science + Business Media |
| record_format | marc |
| spellingShingle | Anderson, David Raymond 1942- Model based inference in the life sciences a primer on evidence Inférence (Logique) Sciences de la vie - Modèles mathématiques Biowissenschaften Mathematisches Modell Inference Life sciences Mathematical models Mathematisches Modell (DE-588)4114528-8 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Biowissenschaften (DE-588)4129772-6 gnd |
| subject_GND | (DE-588)4114528-8 (DE-588)4182963-3 (DE-588)4129772-6 |
| title | Model based inference in the life sciences a primer on evidence |
| title_auth | Model based inference in the life sciences a primer on evidence |
| title_exact_search | Model based inference in the life sciences a primer on evidence |
| title_full | Model based inference in the life sciences a primer on evidence David R. Anderson |
| title_fullStr | Model based inference in the life sciences a primer on evidence David R. Anderson |
| title_full_unstemmed | Model based inference in the life sciences a primer on evidence David R. Anderson |
| title_short | Model based inference in the life sciences |
| title_sort | model based inference in the life sciences a primer on evidence |
| title_sub | a primer on evidence |
| topic | Inférence (Logique) Sciences de la vie - Modèles mathématiques Biowissenschaften Mathematisches Modell Inference Life sciences Mathematical models Mathematisches Modell (DE-588)4114528-8 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Biowissenschaften (DE-588)4129772-6 gnd |
| topic_facet | Inférence (Logique) Sciences de la vie - Modèles mathématiques Biowissenschaften Mathematisches Modell Inference Life sciences Mathematical models Statistische Schlussweise |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016495753&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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