Randomization, bootstrap and Monte Carlo methods in biology:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
Chapman & Hall/CRC
2007
|
| Ausgabe: | 3. ed. |
| Schriftenreihe: | Texts in statistical science
[70] |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | 455 S. graph. Darst. |
| ISBN: | 1584885416 9781584885412 |
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| 650 | 4 | |a Biometry | |
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Datensatz im Suchindex
| _version_ | 1804135421107503104 |
|---|---|
| adam_text | Table
of
Contents
Chapter
1
Randomization
..................................................................1
1.1
The Idea of a Randomization Test
................................................1
1.2
Examples of Randomization Tests
................................................4
1.3
Aspects of Randomization Testing Raised by the
Examples
......................................................................................14
1.3.1
Sampling the Randomization Distribution
or Systematic Enumeration
.............................................15
1.3.2
Equivalent Test Statistics
...............................................16
1.3.3
Significance Levels for Classical
and Randomization Tests
................................................17
1.3.4
Limitations of Randomization Tests
...............................18
1.4
Confidence Limits by Randomization
.........................................18
1.5
Applications of Randomization in Biology
and Related Areas
........................................................................21
1.5.1
Single Species Ecology
.....................................................21
1.5.2
Genetics, Evolution, and Natural Selection
...................22
1.5.3
Community Ecology
.........................................................23
1.5.4
Other Environmental Applications
.................................25
1.6
Randomization and Observational Studies
................................25
1.7
Chapter Summary
...................................................................... 26
Chapter
2
The Jackknife
.................................................................29
2.1
The Jackknife Estimator
.............................................................29
2.2
Applications of Jackknifing in Biology
.......................................35
2.2.1
Single-Species Analyses
...................................................35
2.2.2
Genetics, Evolution, and Natural Selection
...................35
2.2.3
Community Ecology
.........................................................36
2.3
Chapter Summary
.......................................................................40
Chapter
3
The Bootstrap
.................................................................41
3.1
Resampling with Replacement
.................................................41
3.2
Standard Bootstrap Confidence Limits
....................................42
3.3
Simple Percentile Confidence Limits
.......................................46
3.4
Bias-Corrected Percentile Confidence Limits
..........................52
3.5
Accelerated Bias-Corrected Percentile Limits
.........................57
3.6
Other Methods for Constructing Confidence Intervals
..........65
3.7
Transformations to Improve Bootstrap-
1
Intervals
.................68
3.8
Parametric Confidence Intervals
..............................................70
3.9
A Better Estimate of Bias
.........................................................70
3.10
Bootstrap Tests of Significance
.................................................71
3.11
Balanced Bootstrap Sampling
..................................................74
3.12
Applications of Bootstrapping in Biology
.................................74
3.12.1
Single-Species Ecology
.................................................75
3.12.2
Genetics, Evolution, and Natural Selection
...............76
3.12.3
Community Ecology
......................................................77
3.12.4
Other Ecological and Environmental
Applications
..................................................................77
3.13
Further Reading
........................................................................78
3.14
Chapter Summary
.................................................................... 79
Chapter
4
Monte Carlo Methods
.....................................................81
4.1
Monte Carlo Tests
........................................................................81
4.2
Generalized Monte Carlo Tests
...................................................84
4.3
Implicit Statistical Models
..........................................................86
4.4
Applications of Monte Carlo Methods in Biology
......................88
4.4.1
Single-Species Ecology
.....................................................89
4.4.2
Genetics and Evolution
....................................................89
4.4.3
Community Ecology
.........................................................90
4.5
Chapter Summary
.......................................................................90
Chapter
5
Some General Considerations
.......................................93
5.1
Questions about Computer-Intensive Methods
.........................93
5.2
Power
............................................................................................94
5.3
Number of Random Sets of Data Needed for a Test
.................94
5.4
Determining a Randomization Distribution Exactly
................99
5.5
The Number of Replications for Confidence Intervals
...........101
5.6
More Efficient Bootstrap Sampling Methods
..........................103
5.7
The Generation of Pseudo-Random Numbers
.........................103
5.8
The Generation of Random Permutations
...............................104
5.9
Chapter Summary
.....................................................................105
Chapter
6
One- and Two-Sample Tests
........................................107
6.1
The Paired Comparisons Design
............................................107
6.2
The One-Sample Randomization Test
....................................112
6.3
The Two-Sample Randomization Test
....................................113
6.4
Bootstrap Tests
........................................................................116
6.5
Randomizing Residuals
...........................................................117
6.6
Comparing the Variation in Two Samples
.............................119
6.7
A Simulation Study
.................................................................122
6.8
The Comparison of Two Samples on Multiple
Measurements
..........................................................................124
6.9
Further Reading
......................................................................129
6.10
Chapter Summary
...................................................................130
Chapter
7
Analysis of Variance
.....................................................135
7.1
One-Factor Analysis of Variance
...............................................135
7.2
Tests for Constant Variance
......................................................137
7.3
Testing for Mean Differences Using Residuals
.......................138
7.4
Examples of More Complicated Types
of Analysis of Variance
..............................................................143
7.5
Procedures for Handling Unequal Variances
..........................161
7.6
Other Aspects of Analysis of Variance
.....................................162
7.7
Further Reading
.........................................................................163
7.8
Chapter Summary
.....................................................................165
Chapter
8
Regression Analysis
......................................................169
8.1
Simple Linear Regression
.........................................................169
8.2
Randomizing Residuals
.............................................................171
8.3
Testing for a Nonzero
ß
Value
..................................................175
8.4
Confidence Limits for
ß.............................................................175
8.5
Multiple Linear Regression
.......................................................176
8.6
Alternative Randomization Methods with Multiple
Regression
...................................................................................180
8.7
Bootstrapping and Jackknifing with Regression
................... 196
8.8
Further Reading
.........................................................................197
8.9
Chapter Summary
.....................................................................200
Chapter
9
Distance Matrices and Spatial Data
..........................203
9.1
Testing for Association between Distance Matrices
................203
9.2
The Mantel Test
.........................................................................205
9.3
Sampling the Randomization Distribution
..............................207
9.4
Confidence
Limits
for Regression Coefficients
........................210
9.5
The Multiple Mantel Test
..........................................................212
9.6
Other Approaches with More Than Two Matrices
..................213
9.7
Further Reading
.........................................................................230
9.8
Chapter Summary
.....................................................................232
Chapter
10
Other Analyses on Spatial Data
...............................239
10.1
Spatial Data Analysis
............................................................239
10.2
The Study of Spatial Point Patterns
....................................239
10.3
Mead s Randomization Test
...................................................240
10.4
Tests for Randomness Based on Distances
..........................245
10.5
Testing for an Association between Two
Point Patterns
........................................................................247
10.6
The Besag-Diggle Test
..........................................................248
10.7
Tests Using Distances between Points
.................................250
10.8
Testing for Random Marking
................................................252
10.9
Further Reading
.....................................................................255
10.10
Chapter Summary
..................................................................256
Chapter
11
Time Series
.................................................................261
11.1
Randomization and Time Series
...........................................261
11.2
Randomization Tests for Serial Correlation
........................262
11.3
Randomization Tests for Trend
.............................................267
11.4
Randomization Tests for Periodicity
.....................................274
11.5
Irregularly Spaced Series
......................................................281
11.6
Tests on Times of Occurrence
...............................................283
11.7
Discussion on Procedures for Irregular Series
....................285
11.8
Bootstrap Methods
.................................................................290
11.9
Monte Carlo Methods
............................................................290
11.10
Model-Based vs. Moving-Block Resampling
........................292
11.11
Further Reading
.....................................................................294
11.12
Chapter Summary
..................................................................297
Chapter
12
Multivariate Data
.......................................................301
12.1
Univariate and Multivariate Tests
.........................................301
12.2
Sample Mean Vectors and Covariance Matrices
...................301
12.3
Comparison of Sample Mean Vectors
.....................................302
12.4
Chi-Squared Analyses for Count Data
...................................312
12.5
Comparison of Variations for Several Samples
.....................314
12.6
Principal Components Analysis and Other
One-Sample Methods
...............................................................314
12.7
Discriminant
Function Analysis
.............................................317
12.8
Further Reading
......................................................................320
12.9
Chapter Summary
...................................................................321
Chapter
13
Survival and Growth Data
........................................325
13.1
Bootstrapping Survival Data
..................................................325
13.2
Bootstrapping for Variable Selection
......................................327
13.3
Bootstrapping for Model Selection
.........................................329
13.4
Group Comparisons
.................................................................330
13.5
Growth Data
.............................................................................331
13.6
Further Reading
......................................................................336
13.7
Chapter Summary
...................................................................337
Chapter
14
Nonstandard
Situations
.............................................341
14.1
The Construction of Tests in
Nonstandard
Situations
.........341
14.2
Species Co-Occurrences on Islands
........................................341
14.3
Alternative Switching Algorithms
..........................................351
14.4
Examining Time Changes in Niche Overlap
.........................354
14.5
Probing Multivariate Data with Random Skewers
...............360
14.6
Ant Species Sizes in Europe
...................................................365
14.7
Chapter Summary
...................................................................370
Chapter
15
Bayesian Methods
......................................................371
15.1
The Bayesian Approach to Data Analysis
.............................371
15.2
The Gibbs Sampler and Related Methods
.............................372
15.3
Biological Applications
.............................................................377
15.4
Further Reading
......................................................................378
15.5
Chapter Summary
.................................................................. 379
Chapter
16
Final Comments
.........................................................381
16.1
Randomization
.........................................................................381
16.2
Bootstrapping
...........................................................................382
16.3
Monte Carlo Methods in General
...........................................382
16.4
Classical vs. Bayesian Inference
............................................383
References
........................................................................................385
Appendix Software for Computer-Intensive Statistics
................435
Author Index
....................................................................................439
Subject Index
...................................................................................449
STATISTICS
Modern computer-intensive statistical methods play a key role in solving many
problems across a wide range of scientific disciplines. This new edition of the
bestseiling Randomization, Bootstrap and Monte Carlo Methods in Biology
illustrates the value of a number of these methods with an emphasis on biological
applications.
This textbook focuses on three related areas in computational statistics: randomization,
bootstrapping, and Monte Carlo methods of inference. The author emphasizes the
sampling approach within randomization testing and confidence intervals. Similar
to randomization, the book shows how bootstrapping, or resampling, can be used
for confidence intervals and tests of significance. It also explores how to use Monte
Carlo methods to test hypotheses and construct confidence intervals.
New to the Third Edition
>
Updated information on regression and time series analysis, multivariate
methods, survival and growth data, as well as software for computational
statistics
>
References that reflect recent developments in methodology and computing
techniques
>
Additional references on new applications of computer-intensive methods
in biology
Providing comprehensive coverage of computer-intensive applications while also
offering data sets online, Randomization, Bootstrap and Monte Carlo Methods
in Biology, Third Edition supplies a solid foundation for the ever-expanding field
of statistics and quantitative analysis in biology.
Features
•
Presents an overview of computer-intensive statistical methods and applications
in biology
•
Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA,
regression, and Bayesian
•
Features a clear, accessible style that makes if easy for biologists, researchers,
and students to understand the methods discussed
•
Provides information on computer programs and packages to implement
calculations
•
Includes a large number of real, up-to-date examples from a range of disciplines,
particularly in the biological sciences
•
Contains summaries and exercises for each chapter, making it suitable for
courses and self-study
|
| adam_txt |
Table
of
Contents
Chapter
1
Randomization
.1
1.1
The Idea of a Randomization Test
.1
1.2
Examples of Randomization Tests
.4
1.3
Aspects of Randomization Testing Raised by the
Examples
.14
1.3.1
Sampling the Randomization Distribution
or Systematic Enumeration
.15
1.3.2
Equivalent Test Statistics
.16
1.3.3
Significance Levels for Classical
and Randomization Tests
.17
1.3.4
Limitations of Randomization Tests
.18
1.4
Confidence Limits by Randomization
.18
1.5
Applications of Randomization in Biology
and Related Areas
.21
1.5.1
Single Species Ecology
.21
1.5.2
Genetics, Evolution, and Natural Selection
.22
1.5.3
Community Ecology
.23
1.5.4
Other Environmental Applications
.25
1.6
Randomization and Observational Studies
.25
1.7
Chapter Summary
. 26
Chapter
2
The Jackknife
.29
2.1
The Jackknife Estimator
.29
2.2
Applications of Jackknifing in Biology
.35
2.2.1
Single-Species Analyses
.35
2.2.2
Genetics, Evolution, and Natural Selection
.35
2.2.3
Community Ecology
.36
2.3
Chapter Summary
.40
Chapter
3
The Bootstrap
.41
3.1
Resampling with Replacement
.41
3.2
Standard Bootstrap Confidence Limits
.42
3.3
Simple Percentile Confidence Limits
.46
3.4
Bias-Corrected Percentile Confidence Limits
.52
3.5
Accelerated Bias-Corrected Percentile Limits
.57
3.6
Other Methods for Constructing Confidence Intervals
.65
3.7
Transformations to Improve Bootstrap-
1
Intervals
.68
3.8
Parametric Confidence Intervals
.70
3.9
A Better Estimate of Bias
.70
3.10
Bootstrap Tests of Significance
.71
3.11
Balanced Bootstrap Sampling
.74
3.12
Applications of Bootstrapping in Biology
.74
3.12.1
Single-Species Ecology
.75
3.12.2
Genetics, Evolution, and Natural Selection
.76
3.12.3
Community Ecology
.77
3.12.4
Other Ecological and Environmental
Applications
.77
3.13
Further Reading
.78
3.14
Chapter Summary
. 79
Chapter
4
Monte Carlo Methods
.81
4.1
Monte Carlo Tests
.81
4.2
Generalized Monte Carlo Tests
.84
4.3
Implicit Statistical Models
.86
4.4
Applications of Monte Carlo Methods in Biology
.88
4.4.1
Single-Species Ecology
.89
4.4.2
Genetics and Evolution
.89
4.4.3
Community Ecology
.90
4.5
Chapter Summary
.90
Chapter
5
Some General Considerations
.93
5.1
Questions about Computer-Intensive Methods
.93
5.2
Power
.94
5.3
Number of Random Sets of Data Needed for a Test
.94
5.4
Determining a Randomization Distribution Exactly
.99
5.5
The Number of Replications for Confidence Intervals
.101
5.6
More Efficient Bootstrap Sampling Methods
.103
5.7
The Generation of Pseudo-Random Numbers
.103
5.8
The Generation of Random Permutations
.104
5.9
Chapter Summary
.105
Chapter
6
One- and Two-Sample Tests
.107
6.1
The Paired Comparisons Design
.107
6.2
The One-Sample Randomization Test
.112
6.3
The Two-Sample Randomization Test
.113
6.4
Bootstrap Tests
.116
6.5
Randomizing Residuals
.117
6.6
Comparing the Variation in Two Samples
.119
6.7
A Simulation Study
.122
6.8
The Comparison of Two Samples on Multiple
Measurements
.124
6.9
Further Reading
.129
6.10
Chapter Summary
.130
Chapter
7
Analysis of Variance
.135
7.1
One-Factor Analysis of Variance
.135
7.2
Tests for Constant Variance
.137
7.3
Testing for Mean Differences Using Residuals
.138
7.4
Examples of More Complicated Types
of Analysis of Variance
.143
7.5
Procedures for Handling Unequal Variances
.161
7.6
Other Aspects of Analysis of Variance
.162
7.7
Further Reading
.163
7.8
Chapter Summary
.165
Chapter
8
Regression Analysis
.169
8.1
Simple Linear Regression
.169
8.2
Randomizing Residuals
.171
8.3
Testing for a Nonzero
ß
Value
.175
8.4
Confidence Limits for
ß.175
8.5
Multiple Linear Regression
.176
8.6
Alternative Randomization Methods with Multiple
Regression
.180
8.7
Bootstrapping and Jackknifing with Regression
. 196
8.8
Further Reading
.197
8.9
Chapter Summary
.200
Chapter
9
Distance Matrices and Spatial Data
.203
9.1
Testing for Association between Distance Matrices
.203
9.2
The Mantel Test
.205
9.3
Sampling the Randomization Distribution
.207
9.4
Confidence
Limits
for Regression Coefficients
.210
9.5
The Multiple Mantel Test
.212
9.6
Other Approaches with More Than Two Matrices
.213
9.7
Further Reading
.230
9.8
Chapter Summary
.232
Chapter
10
Other Analyses on Spatial Data
.239
10.1
Spatial Data Analysis
.239
10.2
The Study of Spatial Point Patterns
.239
10.3
Mead's Randomization Test
.240
10.4
Tests for Randomness Based on Distances
.245
10.5
Testing for an Association between Two
Point Patterns
.247
10.6
The Besag-Diggle Test
.248
10.7
Tests Using Distances between Points
.250
10.8
Testing for Random Marking
.252
10.9
Further Reading
.255
10.10
Chapter Summary
.256
Chapter
11
Time Series
.261
11.1
Randomization and Time Series
.261
11.2
Randomization Tests for Serial Correlation
.262
11.3
Randomization Tests for Trend
.267
11.4
Randomization Tests for Periodicity
.274
11.5
Irregularly Spaced Series
.281
11.6
Tests on Times of Occurrence
.283
11.7
Discussion on Procedures for Irregular Series
.285
11.8
Bootstrap Methods
.290
11.9
Monte Carlo Methods
.290
11.10
Model-Based vs. Moving-Block Resampling
.292
11.11
Further Reading
.294
11.12
Chapter Summary
.297
Chapter
12
Multivariate Data
.301
12.1
Univariate and Multivariate Tests
.301
12.2
Sample Mean Vectors and Covariance Matrices
.301
12.3
Comparison of Sample Mean Vectors
.302
12.4
Chi-Squared Analyses for Count Data
.312
12.5
Comparison of Variations for Several Samples
.314
12.6
Principal Components Analysis and Other
One-Sample Methods
.314
12.7
Discriminant
Function Analysis
.317
12.8
Further Reading
.320
12.9
Chapter Summary
.321
Chapter
13
Survival and Growth Data
.325
13.1
Bootstrapping Survival Data
.325
13.2
Bootstrapping for Variable Selection
.327
13.3
Bootstrapping for Model Selection
.329
13.4
Group Comparisons
.330
13.5
Growth Data
.331
13.6
Further Reading
.336
13.7
Chapter Summary
.337
Chapter
14
Nonstandard
Situations
.341
14.1
The Construction of Tests in
Nonstandard
Situations
.341
14.2
Species Co-Occurrences on Islands
.341
14.3
Alternative Switching Algorithms
.351
14.4
Examining Time Changes in Niche Overlap
.354
14.5
Probing Multivariate Data with Random Skewers
.360
14.6
Ant Species Sizes in Europe
.365
14.7
Chapter Summary
.370
Chapter
15
Bayesian Methods
.371
15.1
The Bayesian Approach to Data Analysis
.371
15.2
The Gibbs Sampler and Related Methods
.372
15.3
Biological Applications
.377
15.4
Further Reading
.378
15.5
Chapter Summary
. 379
Chapter
16
Final Comments
.381
16.1
Randomization
.381
16.2
Bootstrapping
.382
16.3
Monte Carlo Methods in General
.382
16.4
Classical vs. Bayesian Inference
.383
References
.385
Appendix Software for Computer-Intensive Statistics
.435
Author Index
.439
Subject Index
.449
STATISTICS
Modern computer-intensive statistical methods play a key role in solving many
problems across a wide range of scientific disciplines. This new edition of the
bestseiling Randomization, Bootstrap and Monte Carlo Methods in Biology
illustrates the value of a number of these methods with an emphasis on biological
applications.
This textbook focuses on three related areas in computational statistics: randomization,
bootstrapping, and Monte Carlo methods of inference. The author emphasizes the
sampling approach within randomization testing and confidence intervals. Similar
to randomization, the book shows how bootstrapping, or resampling, can be used
for confidence intervals and tests of significance. It also explores how to use Monte
Carlo methods to test hypotheses and construct confidence intervals.
New to the Third Edition
>
Updated information on regression and time series analysis, multivariate
methods, survival and growth data, as well as software for computational
statistics
>
References that reflect recent developments in methodology and computing
techniques
>
Additional references on new applications of computer-intensive methods
in biology
Providing comprehensive coverage of computer-intensive applications while also
offering data sets online, Randomization, Bootstrap and Monte Carlo Methods
in Biology, Third Edition supplies a solid foundation for the ever-expanding field
of statistics and quantitative analysis in biology.
Features
•
Presents an overview of computer-intensive statistical methods and applications
in biology
•
Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA,
regression, and Bayesian
•
Features a clear, accessible style that makes if easy for biologists, researchers,
and students to understand the methods discussed
•
Provides information on computer programs and packages to implement
calculations
•
Includes a large number of real, up-to-date examples from a range of disciplines,
particularly in the biological sciences
•
Contains summaries and exercises for each chapter, making it suitable for
courses and self-study |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Manly, Bryan F. J. 1944- |
| author_GND | (DE-588)131634062 |
| author_facet | Manly, Bryan F. J. 1944- |
| author_role | aut |
| author_sort | Manly, Bryan F. J. 1944- |
| author_variant | b f j m bfj bfjm |
| building | Verbundindex |
| bvnumber | BV021626211 |
| callnumber-first | Q - Science |
| callnumber-label | QH323 |
| callnumber-raw | QH323.5 |
| callnumber-search | QH323.5 |
| callnumber-sort | QH 3323.5 |
| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | WC 7000 WC 7700 |
| classification_tum | MAT 629f BIO 107f |
| ctrlnum | (OCoLC)70046101 (DE-599)BVBBV021626211 |
| dewey-full | 570.1/5195 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 570 - Biology |
| dewey-raw | 570.1/5195 |
| dewey-search | 570.1/5195 |
| dewey-sort | 3570.1 45195 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Mathematik |
| discipline_str_mv | Biologie Mathematik |
| edition | 3. ed. |
| format | Book |
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| id | DE-604.BV021626211 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:55:20Z |
| indexdate | 2024-07-09T20:40:14Z |
| institution | BVB |
| isbn | 1584885416 9781584885412 |
| language | English |
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| physical | 455 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series | Texts in statistical science |
| series2 | Texts in statistical science |
| spelling | Manly, Bryan F. J. 1944- Verfasser (DE-588)131634062 aut Randomization, bootstrap and Monte Carlo methods in biology Bryan F. J. Manly 3. ed. Boca Raton, Fla. [u.a.] Chapman & Hall/CRC 2007 455 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Texts in statistical science [70] Amostragem larpcal Biometria larpcal Biometrie gtt Biométrie Bootstrap (statistiek) gtt Monte Carlo-methode gtt Monte-Carlo, Méthode de Método de monte carlo larpcal Simulação (aprendizagem) larpcal Échantillonnage (Statistique) Biometry Monte Carlo method Sampling (Statistics) Monte-Carlo-Simulation (DE-588)4240945-7 gnd rswk-swf Resampling (DE-588)4288033-6 gnd rswk-swf Bootstrap-Statistik (DE-588)4139168-8 gnd rswk-swf Randomisierung (DE-588)4280827-3 gnd rswk-swf Biostatistik (DE-588)4729990-3 gnd rswk-swf Biologie (DE-588)4006851-1 gnd rswk-swf Biostatistik (DE-588)4729990-3 s Randomisierung (DE-588)4280827-3 s Bootstrap-Statistik (DE-588)4139168-8 s Monte-Carlo-Simulation (DE-588)4240945-7 s Resampling (DE-588)4288033-6 s DE-604 Biologie (DE-588)4006851-1 s 1\p DE-604 2\p DE-604 Texts in statistical science [70] (DE-604)BV022819715 70 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014841185&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014841185&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 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 |
| spellingShingle | Manly, Bryan F. J. 1944- Randomization, bootstrap and Monte Carlo methods in biology Texts in statistical science Amostragem larpcal Biometria larpcal Biometrie gtt Biométrie Bootstrap (statistiek) gtt Monte Carlo-methode gtt Monte-Carlo, Méthode de Método de monte carlo larpcal Simulação (aprendizagem) larpcal Échantillonnage (Statistique) Biometry Monte Carlo method Sampling (Statistics) Monte-Carlo-Simulation (DE-588)4240945-7 gnd Resampling (DE-588)4288033-6 gnd Bootstrap-Statistik (DE-588)4139168-8 gnd Randomisierung (DE-588)4280827-3 gnd Biostatistik (DE-588)4729990-3 gnd Biologie (DE-588)4006851-1 gnd |
| subject_GND | (DE-588)4240945-7 (DE-588)4288033-6 (DE-588)4139168-8 (DE-588)4280827-3 (DE-588)4729990-3 (DE-588)4006851-1 |
| title | Randomization, bootstrap and Monte Carlo methods in biology |
| title_auth | Randomization, bootstrap and Monte Carlo methods in biology |
| title_exact_search | Randomization, bootstrap and Monte Carlo methods in biology |
| title_exact_search_txtP | Randomization, bootstrap and Monte Carlo methods in biology |
| title_full | Randomization, bootstrap and Monte Carlo methods in biology Bryan F. J. Manly |
| title_fullStr | Randomization, bootstrap and Monte Carlo methods in biology Bryan F. J. Manly |
| title_full_unstemmed | Randomization, bootstrap and Monte Carlo methods in biology Bryan F. J. Manly |
| title_short | Randomization, bootstrap and Monte Carlo methods in biology |
| title_sort | randomization bootstrap and monte carlo methods in biology |
| topic | Amostragem larpcal Biometria larpcal Biometrie gtt Biométrie Bootstrap (statistiek) gtt Monte Carlo-methode gtt Monte-Carlo, Méthode de Método de monte carlo larpcal Simulação (aprendizagem) larpcal Échantillonnage (Statistique) Biometry Monte Carlo method Sampling (Statistics) Monte-Carlo-Simulation (DE-588)4240945-7 gnd Resampling (DE-588)4288033-6 gnd Bootstrap-Statistik (DE-588)4139168-8 gnd Randomisierung (DE-588)4280827-3 gnd Biostatistik (DE-588)4729990-3 gnd Biologie (DE-588)4006851-1 gnd |
| topic_facet | Amostragem Biometria Biometrie Biométrie Bootstrap (statistiek) Monte Carlo-methode Monte-Carlo, Méthode de Método de monte carlo Simulação (aprendizagem) Échantillonnage (Statistique) Biometry Monte Carlo method Sampling (Statistics) Monte-Carlo-Simulation Resampling Bootstrap-Statistik Randomisierung Biostatistik Biologie |
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| volume_link | (DE-604)BV022819715 |
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