Bayesian analysis with stata:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
College Station, Tex.
Stata Press
2014
|
| Ausgabe: | 1st ed. |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027334789&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XX, 279 S. graph. Darst. |
| ISBN: | 9781597181419 1597181412 |
Internformat
MARC
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| 100 | 1 | |a Thompson, John M. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Bayesian analysis with stata |c John Thompson |
| 250 | |a 1st ed. | ||
| 264 | 1 | |a College Station, Tex. |b Stata Press |c 2014 | |
| 300 | |a XX, 279 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-027334789 | |
Datensatz im Suchindex
| _version_ | 1819365013403467776 |
|---|---|
| adam_text | Contents
List of figures
List of tables
Preface
Acknowledgments
1 The problem of priors
1.1 Case study 1: An early phase՝ vaccine trial
1.2 Bayesian calculations....................
1.3 Benefits of a Bayesian analysis .........
1.4 Selecting a good prior...................
1.5 Starting points..........................
1.6 Exercises................................
2 Evaluating the posterior
2.1 Introduction.............................
2.2 Case study 1: The vaccine trial revisited .
2.3 Marginal and conditional distribuí ions . .
2.4 Case study 2: Blood pressure and age . .
2.0 Case study 2: BP and age continued . . .
2.6 General log՝ posteriors..................
2.7 Adding distributions to logdensity ....
2.8 Changing parameterization................
2.9 Starting points..........................
2.10 Exercises................................
3 Metropolis-Hastings
3.1 Introduction.............................
Xlii
xvii
xix
xxi
1
1
2
1
r
7
(S
9
!)
9
11
12
17
19
21
23
24
24
27
27
Contents
VIII
29
:i.2 The Mil algorithm in Stata.........................................
30
3.3 Flu* nilis commands .............................................
31
;U Case study . 3: Polyp counts.........................................
a.a Scaling the proposal distribution................................... 36
37
The mnnmm command................................................
.3.7 Multi para i lie tor models......................................... 39
3.S Case study 3: Polyp counts continued ............................... 39
3.9 Highly correlated parameters........................................ 44
3.9.1 Centering................................................ 44
3.9.2 Block updating............................................. 47
3.10 Case st udy 3: Polyp counts yet again............................... 48
3.11 Starting points..................................................... 49
3.12 Kxercises........................................................... 50
Gibbs sampling 53
1.1 Introduction...................................................... 53
1.2 Case study 4: A regression model for pain scores............... 54
1.3 Conjugate priors.................................................... 59
11 (libhs sampling with nonstandard distributions...................... 59
1.1.1 (Iriddy sampling........................................... 60
1. 1.2 Slice sampling............................................. 61
1.1.3 Adaptive rejection ........................................ 63
1.3 The gbs commands.................................................... 66
Mi Case study 1 continued: Laplace regression ......................... 67
1.7 Starting points..................................................... 71
l.s Kxereises..................................... 72
Assessing convergence 75
3.1 Introduction........................................................ 75
3.2 Detecting early drift .............................................. 75
3 Detecting too short a run..................... gQ
3.3.1 Thinning the chain......................................... gl
Coil fonts
IX
6
7
5.4 Running multiple՝ chains..............................
5.5 Convergence of functions of (Ik* parameters...........
5.6 Case study 5: Beta-blocker trials.....................
5.7 Further reading.......................................
5.8 Exercises.............................................
Validating the Stata code and summarizing the results
6.1 Introduction..........................................
6.2 Case study G: Ordinal regression......................
6.3 Validating the soft wan՝..............................
6.4 Numerical summaries...................................
6.5 Graphical summaries...................................
6.6 Further reading.......................................
6.7 Exercises.............................................
Bayesian analysis with Mata
7.1 Introduction..........................................
7.2 The basics of Mata....................................
7.3 Case study (i: Revisited .............................
7.4 Case study 7: Germination of՛ broomrape ..............
7.4.1 Tuning the proposal distributions.............
7.1.2 Using conditional (listributions .............
7.4.3 More efficient computation....................
7.4.4 Hierarchical centering........................
7.4.5 Gibbs sampling................................
7.4.0 Slice. Griddy. and ARMS sampling..............
7.4.7 Timings.......................................
7.4.8 Adding new densities to logdensiiyt)..........
7.5 Further reading.......................................
7.6 Exercises.............................................
83
85
85
90
90
93
93
93
97
100
104
108
108
111
111
ill
110
118
121
122
123
124
125
120
120
128
129
129
131
8 Using WinBUGS for model fitting
8.1 Introduction...............
131
132
133
133
135
136
137
138
138
139
139
140
144
146
150
151
152
152
152
154
155
155
156
157
159
159
161
161
Installing th * software...................................
8.2.1 Installing OpenBUGS ................................
8.2.2 Installing WinBUGS..................................
Preparing a WinBUGS analysis...............................
8.3.1 The model file......................................
8.3.2 The data file.....................................
8.3.3 The initial values file.............................
8.3.1 The script file.....................................
8.3.5 Running the script..................................
8.3.6 Reading the results into Stata......................
8.3.7 Inspecting the log file.............................
8.3.8 Reading WinBUGS data files..........................
Gase study 8: Growth of sea cows........................... .
8.1.1 WinBUGS or OpenBUGS.................................
Cast՝ study 9: Jawbone size................................
8.5.1 Over relaxation ....................................
8.5.2 (’hanging the seed for the random-number generator .
Advanced feat tires of WinBUGS ............................
8.6.1 Missing data........................................
8.6.2 Censoring and truncation ...........................
8.6.3 Nonstandard likelihoods.............................
8.6.1 Nonstandard priors..................................
8.6.5 The eut() function..................................
GeoBUGS....................................................
Programming a series of Bayesian analyses..................
OpenBUGS under Linux.........................
Debugging WinBUGS............................
Starting points........................
Exercises..........
xi
9 Model checking 163
9.1 Introduction....................................................... 163
9.2 Bayesian residual analysis......................................... 163
9.3 The meinccheck command............................................. 165
9.4 Case study 10: Models for Salmonella assays ..................... 166
9.4.1 Generating the predictions in WinBUGS..................... 167
9.4.2 Plotting the predictive distributions..................... 169
9.4.3 Residual plots............................................ 170
9.4.4 Empirical probability plots............................... 174
9.4.5 A summary plot............................................ 177
9.5 Residual checking with Stata ...................................... 179
9.6 Residual checking with Mata ....................................... 180
9.7 Further reading.................................................... 182
9.8 Exercises.......................................................... 182
10 Model selection 185
10.1 Introduction....................................................... 185
10.2 Case study 11: Choosing a genetic model ........................... 186
10.2.1 Plausible models ......................................... 187
10.2.2 Bayes factors............................................. 188
10.3 Calculating a BF................................................... 189
10.4 Calculating the BFs for the XTD ease study...................... 191
10.5 Robustness of the BF............................................... 199
10.6 Model averaging ................................................... 199
10.7 Information criteria............................................... 201
10.8 DIC for the genetic models ........................................ 203
10.9 Starting points.................................................... 204
10.10 Exercises......................................................... 204
11 Further case studies 205
11.1 Introduction....................................................... 205
11.2 Case study 12: Modeling cancer incidence........................... 205
212
219
228
235
237
237
237
239
242
244
245
246
249
250
251
265
273
277
11.3 Case study 13: Creatinine clearance .....................
11.1 Case study 14: Microarray experiment.....................
11.5 Case study 15: Recurrent asthma attacks .................
11. (i Exercises...............................................
Writing Stata programs for specific Bayesian analysis
12.1 Introduction.............................................
12.2 The Bayesian lasso.......................................
12.3 The Gibbs sampler........................................
12,1 The Mata code............................................
12.5 A Stata ado-file.........................................
12.6 Testing the code ........................................
12.7 Case study 16: Diabetes data.............................
12.S Extensions to the Bayesian lasso program.................
12.9 Exercises................................................
Standard distributions
References
Author index
Subject index
|
| any_adam_object | 1 |
| author | Thompson, John M. |
| author_facet | Thompson, John M. |
| author_role | aut |
| author_sort | Thompson, John M. |
| author_variant | j m t jm jmt |
| building | Verbundindex |
| bvnumber | BV041890860 |
| classification_rvk | QH 233 ST 601 |
| ctrlnum | (OCoLC)879606378 (DE-599)BVBBV041890860 |
| discipline | Informatik Wirtschaftswissenschaften |
| edition | 1st ed. |
| format | Book |
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| id | DE-604.BV041890860 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:57:07Z |
| institution | BVB |
| isbn | 9781597181419 1597181412 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027334789 |
| oclc_num | 879606378 |
| open_access_boolean | |
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| physical | XX, 279 S. graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Stata Press |
| record_format | marc |
| spellingShingle | Thompson, John M. Bayesian analysis with stata Stata (DE-588)4617285-3 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4617285-3 (DE-588)4144220-9 |
| title | Bayesian analysis with stata |
| title_auth | Bayesian analysis with stata |
| title_exact_search | Bayesian analysis with stata |
| title_full | Bayesian analysis with stata John Thompson |
| title_fullStr | Bayesian analysis with stata John Thompson |
| title_full_unstemmed | Bayesian analysis with stata John Thompson |
| title_short | Bayesian analysis with stata |
| title_sort | bayesian analysis with stata |
| topic | Stata (DE-588)4617285-3 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Stata Bayes-Entscheidungstheorie |
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