Bootstrap methods: a guide for practitioners and researchers
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, NJ
Wiley
2008
|
| Edition: | 2. ed. |
| Series: | Wiley series in probability and statistics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 188 - 329 |
| Physical Description: | XVIII, 369 S. Ill., graph. Darst. |
| ISBN: | 9780471756217 |
Staff View
MARC
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| 245 | 1 | 0 | |a Bootstrap methods |b a guide for practitioners and researchers |c Michael R. Chernick |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2008 | |
| 300 | |a XVIII, 369 S. |b Ill., graph. Darst. | ||
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| 490 | 0 | |a Wiley series in probability and statistics | |
| 500 | |a Literaturverz. S. 188 - 329 | ||
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Record in the Search Index
| _version_ | 1804137334569959424 |
|---|---|
| adam_text | Contents
Preface
to Second Edition
ix
Preface to First Edition
xiii
Acknowledgments
xvii
1.
What Is Bootstrapping?
1
1.1.
Background,
1
1.2.
Introduction,
8
1.3.
Wide Range of Applications,
13
1.4.
Historical Notes,
16
1.5.
Summary,
24
2.
Estimation
26
2.1.
Estimating Bias,
26
2.1.1.
How to Do It by Bootstrapping,
26
2.1.2.
Error Rate Estimation in Discrimination,
28
2.1.3.
Error Rate Estimation: An Illustrative Problem,
39
2.1.4.
Efron s Patch Data Example,
44
2.2.
Estimating Location and Dispersion,
46
2.2.1.
Means and Medians,
47
2.2.2.
Standard Errors and Quartiles,
48
2.3.
Historical Notes,
51
3.
Confidence Sets and Hypothesis Testing
53
3.1.
Confidence Sets,
55
3.1.1.
Typical Value Theorems for M-Estimates,
55
3.1.2.
Percentile Method,
57
y¡
CONTENTS
3.1.3.
Bias Correction and the Acceleration Constant,
58
3.1.4.
Iterated Bootstrap,
61
3.1.5.
Bootstrap
Percentik í
Confidence Intervals,
64
3.2.
Relationship Between Confidence Intervals and Tests of
Hypotheses,
64
3.3.
Hypothesis Testing Problems,
66
3.3.1.
Tendril DX Lead Clinical Trial Analysis,
67
3.4.
An Application of Bootstrap Confidence Intervals to Binary
Dose-Response Modeling,
71
3.5.
Historical Notes,
75
4.
Regression Analysis
78
4.1.
Linear Models,
82
4.1.1.
Gauss-Markov Theory,
83
4.1.2.
Why Not Just Use Least Squares?
83
4.1.3.
Should I Bootstrap the Residuals from the Fit?
84
4.2.
Nonlinear Models,
86
4.2.1.
Examples of Nonlinear Models,
87
4.2.2.
A Quasi-optical Experiment,
89
4.3.
Nonparametric Models,
93
4.4.
Historical Notes,
94
5.
Forecasting and Time Series Analysis
97
5.1.
Methods of Forecasting,
97
5.2.
Time Series Models,
98
5.3.
When Does Bootstrapping Help with Prediction Intervals?
99
5.4.
Model-Based Versus Block Resampling,
103
5.5.
Explosive
Autoregressive
Processes,
107
5.6.
Bootstrapping-Stationary
Arma
Models,
108
5.7.
Frequency-Based Approaches,
108
5.8.
Sieve Bootstrap,
110
5.9.
Historical Notes, 111
6.
Which Resampling Method Should You Use?
114
6.1.
Related Methods,
115
6.1.1.
Jackknife,
115
6.1.2.
Delta Method, Infinitesimal Jackknife, and Influence
Functions,
116
6.1.3.
Cross-Validation,
119
6.1.4.
Subsampling,
119
CONTENTS
VU
6.2.
Bootstrap Variants,
120
6.2.1.
Bayesian Bootstrap,
121
6.2.2.
The Smoothed Boostrap,
123
6.2.3.
The Parametric Bootstrap,
124
6.2.4.
Double Bootstrap,
125
6.2.5.
The m-out-of-n Bootstrap,
125
7.
Efficient and Effective Simulation
127
7.1.
How Many Replications?
128
7.2.
Variance Reduction Methods,
129
7.2.1.
Linear Approximation,
129
7.2.2.
Balanced Resampling,
131
7.2.3.
Antithetic
Variâtes,
132
7.2.4.
Importance Sampling,
133
7.2.5.
Centering,
134
7.3.
When Can Monte Carlo Be Avoided?
135
7.4.
Historical Notes,
136
8.
Special Topics
1
8.1.
Spatial Data,
139
8.1.1.
Kriging,
139
8.1.2.
Block Bootstrap on Regular Grids,
142
8.1.3.
Block Bootstrap on Irregular Grids,
143
8.2.
Subset Selection,
143
8.3.
Determining the Number of Distributions in a Mixture
Model,
145
8.4.
Censored Data,
148
8.5.
p-Value Adjustment,
149
8.5.1.
Description of Westfall-Young Approach,
150
8.5.2.
Passive Plus DX Example,
150
8.5.3.
Consulting Example,
152
8.6.
Bioequivalence Applications,
153
8.6.1.
Individual Bioequivalence,
153
8.6.2.
Population Bioequivalence,
155
8.7.
Process Capability Indices,
156
8.8.
Missing Data,
164
8.9.
Point Processes,
166
8.10.
Lattice Variables,
168
8.11.
Historical Notes,
169
v¡¡¡
CONTENTS
9.
When Bootstrapping Fails Along with Remedies for Failures
172
9.1.
Too Small of a Sample Size,
173
9.2.
Distributions with Infinite Moments,
175
9.2.1.
Introduction,
175
9.2.2.
Example of Inconsistency,
176
9.2.3.
Remedies,
176
9.3.
Estimating Extreme Values,
177
9.3.1.
Introduction,
177
9.3.2.
Example of Inconsistency,
177
9.3.3.
Remedies,
178
9.4.
Survey Sampling,
179
9.4.1.
Introduction,
179
9.4.2.
Example of Inconsistency,
180
9.4.3.
Remedies,
180
9.5.
Data Sequences that Are M-Dependent,
180
9.5.1.
Introduction,
180
9.5.2.
Example of Inconsistency When Independence Is
Assumed,
181
9.5.3.
Remedies,
181
9.6.
Unstable
Autoregressive
Processes,
182
9.6.1.
Introduction,
182
9.6.2.
Example of Inconsistency,
182
9.6.3.
Remedies,
183
9.7.
Long-Range Dependence,
183
9.7.1.
Introduction,
183
9.7.2.
Example of Inconsistency,
183
9.7.3.
Remedies,
184
9.8.
Bootstrap Diagnostics,
184
9.9.
Historical Notes,
185
Bibliography
1
(Prior to
1999) 188
Bibliography
2 (1999-2007) 274
Author Index
330
Subject Index
359
|
| adam_txt |
Contents
Preface
to Second Edition
ix
Preface to First Edition
xiii
Acknowledgments
xvii
1.
What Is Bootstrapping?
1
1.1.
Background,
1
1.2.
Introduction,
8
1.3.
Wide Range of Applications,
13
1.4.
Historical Notes,
16
1.5.
Summary,
24
2.
Estimation
26
2.1.
Estimating Bias,
26
2.1.1.
How to Do It by Bootstrapping,
26
2.1.2.
Error Rate Estimation in Discrimination,
28
2.1.3.
Error Rate Estimation: An Illustrative Problem,
39
2.1.4.
Efron's Patch Data Example,
44
2.2.
Estimating Location and Dispersion,
46
2.2.1.
Means and Medians,
47
2.2.2.
Standard Errors and Quartiles,
48
2.3.
Historical Notes,
51
3.
Confidence Sets and Hypothesis Testing
53
3.1.
Confidence Sets,
55
3.1.1.
Typical Value Theorems for M-Estimates,
55
3.1.2.
Percentile Method,
57
y¡
CONTENTS
3.1.3.
Bias Correction and the Acceleration Constant,
58
3.1.4.
Iterated Bootstrap,
61
3.1.5.
Bootstrap
Percentik í
Confidence Intervals,
64
3.2.
Relationship Between Confidence Intervals and Tests of
Hypotheses,
64
3.3.
Hypothesis Testing Problems,
66
3.3.1.
Tendril DX Lead Clinical Trial Analysis,
67
3.4.
An Application of Bootstrap Confidence Intervals to Binary
Dose-Response Modeling,
71
3.5.
Historical Notes,
75
4.
Regression Analysis
78
4.1.
Linear Models,
82
4.1.1.
Gauss-Markov Theory,
83
4.1.2.
Why Not Just Use Least Squares?
83
4.1.3.
Should I Bootstrap the Residuals from the Fit?
84
4.2.
Nonlinear Models,
86
4.2.1.
Examples of Nonlinear Models,
87
4.2.2.
A Quasi-optical Experiment,
89
4.3.
Nonparametric Models,
93
4.4.
Historical Notes,
94
5.
Forecasting and Time Series Analysis
97
5.1.
Methods of Forecasting,
97
5.2.
Time Series Models,
98
5.3.
When Does Bootstrapping Help with Prediction Intervals?
99
5.4.
Model-Based Versus Block Resampling,
103
5.5.
Explosive
Autoregressive
Processes,
107
5.6.
Bootstrapping-Stationary
Arma
Models,
108
5.7.
Frequency-Based Approaches,
108
5.8.
Sieve Bootstrap,
110
5.9.
Historical Notes, 111
6.
Which Resampling Method Should You Use?
114
6.1.
Related Methods,
115
6.1.1.
Jackknife,
115
6.1.2.
Delta Method, Infinitesimal Jackknife, and Influence
Functions,
116
6.1.3.
Cross-Validation,
119
6.1.4.
Subsampling,
119
CONTENTS
VU
6.2.
Bootstrap Variants,
120
6.2.1.
Bayesian Bootstrap,
121
6.2.2.
The Smoothed Boostrap,
123
6.2.3.
The Parametric Bootstrap,
124
6.2.4.
Double Bootstrap,
125
6.2.5.
The m-out-of-n Bootstrap,
125
7.
Efficient and Effective Simulation
127
7.1.
How Many Replications?
128
7.2.
Variance Reduction Methods,
129
7.2.1.
Linear Approximation,
129
7.2.2.
Balanced Resampling,
131
7.2.3.
Antithetic
Variâtes,
132
7.2.4.
Importance Sampling,
133
7.2.5.
Centering,
134
7.3.
When Can Monte Carlo Be Avoided?
135
7.4.
Historical Notes,
136
8.
Special Topics
1
8.1.
Spatial Data,
139
8.1.1.
Kriging,
139
8.1.2.
Block Bootstrap on Regular Grids,
142
8.1.3.
Block Bootstrap on Irregular Grids,
143
8.2.
Subset Selection,
143
8.3.
Determining the Number of Distributions in a Mixture
Model,
145
8.4.
Censored Data,
148
8.5.
p-Value Adjustment,
149
8.5.1.
Description of Westfall-Young Approach,
150
8.5.2.
Passive Plus DX Example,
150
8.5.3.
Consulting Example,
152
8.6.
Bioequivalence Applications,
153
8.6.1.
Individual Bioequivalence,
153
8.6.2.
Population Bioequivalence,
155
8.7.
Process Capability Indices,
156
8.8.
Missing Data,
164
8.9.
Point Processes,
166
8.10.
Lattice Variables,
168
8.11.
Historical Notes,
169
v¡¡¡
CONTENTS
9.
When Bootstrapping Fails Along with Remedies for Failures
172
9.1.
Too Small of a Sample Size,
173
9.2.
Distributions with Infinite Moments,
175
9.2.1.
Introduction,
175
9.2.2.
Example of Inconsistency,
176
9.2.3.
Remedies,
176
9.3.
Estimating Extreme Values,
177
9.3.1.
Introduction,
177
9.3.2.
Example of Inconsistency,
177
9.3.3.
Remedies,
178
9.4.
Survey Sampling,
179
9.4.1.
Introduction,
179
9.4.2.
Example of Inconsistency,
180
9.4.3.
Remedies,
180
9.5.
Data Sequences that Are M-Dependent,
180
9.5.1.
Introduction,
180
9.5.2.
Example of Inconsistency When Independence Is
Assumed,
181
9.5.3.
Remedies,
181
9.6.
Unstable
Autoregressive
Processes,
182
9.6.1.
Introduction,
182
9.6.2.
Example of Inconsistency,
182
9.6.3.
Remedies,
183
9.7.
Long-Range Dependence,
183
9.7.1.
Introduction,
183
9.7.2.
Example of Inconsistency,
183
9.7.3.
Remedies,
184
9.8.
Bootstrap Diagnostics,
184
9.9.
Historical Notes,
185
Bibliography
1
(Prior to
1999) 188
Bibliography
2 (1999-2007) 274
Author Index
330
Subject Index
359 |
| any_adam_object | 1 |
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| author | Chernick, Michael R. 1947- |
| author_GND | (DE-588)136029752 |
| author_facet | Chernick, Michael R. 1947- |
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| author_sort | Chernick, Michael R. 1947- |
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| building | Verbundindex |
| bvnumber | BV023084957 |
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| callnumber-subject | QA - Mathematics |
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| classification_tum | MAT 629f |
| ctrlnum | (OCoLC)156785095 (DE-599)BVBBV023084957 |
| dewey-full | 519.5/44 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/44 |
| dewey-search | 519.5/44 |
| dewey-sort | 3519.5 244 |
| dewey-tens | 510 - Mathematics |
| discipline | Psychologie Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Psychologie Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV023084957 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:38:48Z |
| indexdate | 2024-07-09T21:10:39Z |
| institution | BVB |
| isbn | 9780471756217 |
| language | English |
| lccn | 2007029309 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016287922 |
| oclc_num | 156785095 |
| open_access_boolean | |
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| owner_facet | DE-N2 DE-91G DE-BY-TUM DE-355 DE-BY-UBR DE-29T DE-11 DE-945 DE-578 DE-91 DE-BY-TUM DE-523 |
| physical | XVIII, 369 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Chernick, Michael R. 1947- Verfasser (DE-588)136029752 aut Bootstrap methods a guide for practitioners and researchers Michael R. Chernick 2. ed. Hoboken, NJ Wiley 2008 XVIII, 369 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Literaturverz. S. 188 - 329 Bootstrap (Statistics) Biomechanik (DE-588)4006880-8 gnd rswk-swf Bootstrap-Statistik (DE-588)4139168-8 gnd rswk-swf Bootstrap-Statistik (DE-588)4139168-8 s Biomechanik (DE-588)4006880-8 s 1\p DE-604 Erscheint auch als Online-Ausgabe 978-0-470-19257-3 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016287922&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 |
| spellingShingle | Chernick, Michael R. 1947- Bootstrap methods a guide for practitioners and researchers Bootstrap (Statistics) Biomechanik (DE-588)4006880-8 gnd Bootstrap-Statistik (DE-588)4139168-8 gnd |
| subject_GND | (DE-588)4006880-8 (DE-588)4139168-8 |
| title | Bootstrap methods a guide for practitioners and researchers |
| title_auth | Bootstrap methods a guide for practitioners and researchers |
| title_exact_search | Bootstrap methods a guide for practitioners and researchers |
| title_exact_search_txtP | Bootstrap methods a guide for practitioners and researchers |
| title_full | Bootstrap methods a guide for practitioners and researchers Michael R. Chernick |
| title_fullStr | Bootstrap methods a guide for practitioners and researchers Michael R. Chernick |
| title_full_unstemmed | Bootstrap methods a guide for practitioners and researchers Michael R. Chernick |
| title_short | Bootstrap methods |
| title_sort | bootstrap methods a guide for practitioners and researchers |
| title_sub | a guide for practitioners and researchers |
| topic | Bootstrap (Statistics) Biomechanik (DE-588)4006880-8 gnd Bootstrap-Statistik (DE-588)4139168-8 gnd |
| topic_facet | Bootstrap (Statistics) Biomechanik Bootstrap-Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016287922&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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