A practitioner's guide to resampling for data analysis, data mining, and modeling:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Pr.
2012
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027669139&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | X, 214 S. graph. Darst. |
| ISBN: | 9781439855508 |
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Datensatz im Suchindex
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| adam_text | Titel: A practitioner s guide to resampling for data analysis, data mining, and modeling
Autor: Good, Phillip I
Jahr: 2012
Contents
Preface.
..IX
1 Wide Range of Applications.........................................................................1
Resampling Methods......................................................................................1
Fields of Application........................................................................................2
2 Estimation and the Bootstrap.......................................................................7
Precision of an Estimate..................................................................................7
Stata.............................................................................................................10
Applying the Bootstrap............................................................................10
Which Statistic Should We Use?.............................................................10
Confidence Intervals......................................................................................12
When Variances Cannot Be Assumed to Be the Same........................12
R...................................................................................................................13
Stata.............................................................................................................13
Testing for Equivalence............................................................................14
Improved Confidence Intervals...................................................................15
Bias-Corrected Bootstrap Confidence Interval.....................................15
Computer Code: The Bias-Corrected and -Accelerated Bootstrap.... 16
R..............................................................................................................16
SAS..........................................................................................................17
S-Phis......................................................................................................17
Stata........................................................................................................17
Balanced Bootstrap...................................................................................17
Tilted Bootstrap.........................................................................................18
Block Bootstrap..........................................................................................18
Iterated Bootstrap......................................................................................19
When the Form of the Distribution Is Known......................................20
Estimating Bias...............................................................................................20
An Example................................................................................................21
Determining Sample Size.............................................................................22
Original Sample........................................................................................22
Bootstrap Sample......................................................................................23
Summary.........................................................................................................24
To Learn More................................................................................................24
Exercises..........................................................................................................25
3 Software for Use with the Bootstrap and Permutation Tests..............27
AFNI................................................................................................................27
Blossom Statistical Analysis Package.........................................................27
iii
iv Contents
Eviews..............................................................................................................28
HaploView.......................................................................................................28
MATLAB®.......................................................................................................28
NCSS................................................................................................................28
PAUP................................................................................................................29
R........................................................................................................................29
SAS...................................................................................................................29
S-Plus...............................................................................................................30
SPSS Exact Tests.............................................................................................30
Stata..................................................................................................................30
Statistical Calculator......................................................................................30
StatXact............................................................................................................31
Testimate.........................................................................................................31
4 Comparing Two Populations......................................................................33
A Distribution-Free Test................................................................................33
A Little Math..............................................................................................35
Some Statistical Considerations...................................................................35
Framing the Hypothesis..........................................................................36
Hypothesis vs. Alternative......................................................................36
Assumptions..............................................................................................37
General Hypotheses.................................................................................38
Computing the p-Value.................................................................................39
Monte Carlo................................................................................................39
R..............................................................................................................40
SPLUS.....................................................................................................40
STATA....................................................................................................40
Other Two-Sample Comparisons................................................................41
Two-Sided Test...........................................................................................41
Rank Tests..................................................................................................42
Matched Pairs............................................................................................42
RCode....................................................................................................43
Stata........................................................................................................44
Test for Nonequivalence...........................................................................44
Underlying Assumptions.........................................................................45
Comparing Variances....................................................................................45
R Code for Aly s Test Statistic..................................................................47
Unequal Sample Sizes..............................................................................48
Preferred Method......................................................................................48
RCode....................................................................................................49
Testing in the Presence of Nonresponders............................................50
Summary.........................................................................................................51
To Learn More................................................................................................51
Exercises..........................................................................................................52
Contents
5 Multiple Variables........................................................................................55
Single-Valued Test Statistic...........................................................................55
Hotelling s T2.............................................................................................55
Application to Repeated Measures....................................................57
The Generalized Quadratic Form...........................................................58
Application to Epidemiology..............................................................58
Further Generalization........................................................................59
The MRPP Statistic....................................................................................59
Analyzing Migration Data..................................................................60
Gene Set Enrichment Analysis...............................................................61
Combining Univariate Tests.........................................................................62
Pesarin s Nonparametric Combination..................................................64
Summary.........................................................................................................65
To Learn More................................................................................................65
Exercises..........................................................................................................66
6 Experimental Design and Analysis..........................................................69
Separating Signal from Noise......................................................................69
Blocking......................................................................................................70
Analyzing a Blocked Experiment...........................................................71
Combining Data to Obtain Improved Estimates.............................71
Comparing Samples from Two Populations....................................72
Randomization..........................................................................................73
A:-Sample Comparison...................................................................................74
Testing for Any and All Differences among Means............................74
Testing for Any and All Differences among Variances.......................75
R..............................................................................................................76
Stata........................................................................................................77
Ordered Alternatives................................................................................77
Multiple Factors..............................................................................................78
Main Effects...............................................................................................79
Testing for Interactions.............................................................................81
Eliminating the Effects of Multiple Covariates.........................................82
Latin Squares.............................................................................................83
Crossover Designs.........................................................................................86
Analysis of a Complete Balanced Design..............................................87
Analysis of a Balanced Design When Not All Subjects
Complete Treatment.................................................................................88
Which Sets of Labels Should We Rearrange?............................................88
Determining Sample Size.............................................................................89
Missing Combinations..................................................................................89
Summary.........................................................................................................91
To Learn More................................................................................-..............91
Exercises..........................................................................................................92
vi Contents
7 Categorical Data............................................................................................97
Fisher s Exact Test..........................................................................................97
Computing Fisher s Exact Test................................................................99
R............................................................................................................100
Two-Tailed Tests......................................................................................100
Borderline Significance..........................................................................102
Is the Sample Large Enough?................................................................103
Odds Ratio....................................................................................................104
Stratified 2 x 2s........................................................................................106
Controlling the False Discovery Rate...................................................107
Unordered rxc Contingency Tables.........................................................107
Test of Association..................................................................................109
Causation vs. Association......................................................................Ill
Ordered Statistical Tables...........................................................................112
Partial Dependence.................................................................................113
Correspondence Analysis.................................................................114
More than Two Rows and Two Columns............................................114
Singly Ordered Tables.......................................................................114
Doubly Ordered Tables......................................................................116
Multidimensional Arrays...........................................................................116
Summary.......................................................................................................117
To Learn More..............................................................................................118
Exercises........................................................................................................118
8 Multiple Hypotheses..................................................................................121
Controlling the Family-Wise Error Rate...................................................121
Microarray Analysis...............................................................................122
EEG Analysis...........................................................................................122
Controlling the False Discovery Rate.......................................................123
Software for Performing Multiple Simultaneous Tests..........................124
AFNI.........................................................................................................124
ExactFDR..................................................................................................124
NPCtest.....................................................................................................125
R.................................................................................................................125
SAS............................................................................................................125
Testing for Trend..........................................................................................125
Summary.......................................................................................................127
To Learn More..............................................................................................127
9 Model Building...........................................................................................129
Regression Models.......................................................................................129
Bivariate Dependence.............................................................................131
Applying the Permutation Test..................................................................131
Models with a Single Predictor.............................................................132
Contents vii
Comparing Two Regression Lines........................................................132
Multipredictor Regression.....................................................................134
Adaptive Regression...............................................................................136
Applying the Bootstrap...............................................................................137
Stata...........................................................................................................138
Building a Model.....................................................................................139
Limitations of the Bootstrap..................................................................140
Prediction Error............................................................................................140
Cross-Validation......................................................................................141
Double Bootstrap.....................................................................................141
Validation......................................................................................................141
Metrics......................................................................................................142
Nearest Neighbors..................................................................................142
Goodness of Fit........................................................................................143
Using the Bootstrap for Model Validation...........................................144
RCode..................................................................................................145
Cross-Validation......................................................................................145
Summary.......................................................................................................146
To Learn More..............................................................................................146
Exercises........................................................................................................147
10 Classification...............................................................................................149
Cluster Analysis...........................................................................................149
Classification.................................................................................................151
Decision Trees..............................................................................................154
Refining the Model.................................................................................155
Decision Trees vs. Regression....................................................................155
Which Predictors?...................................................................................158
Which Decision Tree Algorithm Is Best for Your Application?............159
Some Comparisons.................................................................................163
Reducing the Rate of Misclassification.....................................................163
Boosting....................................................................................................163
AdaBoost Algorithm...............................................................................167
Ensemble Methods..................................................................................167
Comparison of Classification Tree Algorithms.......................................168
Validation vs. Cross-Validation..................................................................170
Summary.......................................................................................................170
To Learn More..............................................................................................171
Exercises........................................................................................................172
11 Restricted Permutations............................................................................173
Quasi Independence....................................................................................173
Complete Factorials.....................................................................................174
viii Contents
Synchronized Permutations.......................................................................175
Generalizing These Results to Multiple Factors.................................177
Algorithms...............................................................................................179
Which Test Should We Use?..................................................................180
Model Validation..........................................................................................180
Exercises........................................................................................................181
Appendix A: Basic Concepts in Statistics.....................................................183
Additive vs. Multiplicative Models...........................................................183
Central Values..............................................................................................183
Combinations and Rearrangements.........................................................184
Dispersion.....................................................................................................184
Frequency Distribution and Percentiles...................................................185
Linear vs. Nonlinear Regression...............................................................185
Regression Methods....................................................................................186
Appendix B: Proof of Theorems.....................................................................187
References...........................................................................................................193
Index.....................................................................................................................211
|
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| isbn | 9781439855508 |
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| spellingShingle | Good, Phillip I. 1937- A practitioner's guide to resampling for data analysis, data mining, and modeling Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd Resampling (DE-588)4288033-6 gnd Modellierung (DE-588)4170297-9 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4123037-1 (DE-588)4288033-6 (DE-588)4170297-9 |
| title | A practitioner's guide to resampling for data analysis, data mining, and modeling |
| title_auth | A practitioner's guide to resampling for data analysis, data mining, and modeling |
| title_exact_search | A practitioner's guide to resampling for data analysis, data mining, and modeling |
| title_full | A practitioner's guide to resampling for data analysis, data mining, and modeling Phillip I. Good |
| title_fullStr | A practitioner's guide to resampling for data analysis, data mining, and modeling Phillip I. Good |
| title_full_unstemmed | A practitioner's guide to resampling for data analysis, data mining, and modeling Phillip I. Good |
| title_short | A practitioner's guide to resampling for data analysis, data mining, and modeling |
| title_sort | a practitioner s guide to resampling for data analysis data mining and modeling |
| topic | Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd Resampling (DE-588)4288033-6 gnd Modellierung (DE-588)4170297-9 gnd |
| topic_facet | Data Mining Datenanalyse Resampling Modellierung |
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