Bayesian statistics and marketing:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2006
|
| Ausgabe: | Repr. with corr. |
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | X, 348 S. graph. Darst. |
| ISBN: | 0470863676 9780470863671 |
Internformat
MARC
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| 100 | 1 | |a Rossi, Peter E. |d 1955- |e Verfasser |0 (DE-588)141279192 |4 aut | |
| 245 | 1 | 0 | |a Bayesian statistics and marketing |c Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch |
| 250 | |a Repr. with corr. | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2006 | |
| 300 | |a X, 348 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series in probability and statistics | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Marketing research |x Mathematical models | |
| 650 | 4 | |a Marketing |x Mathematical models | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1804136413574201344 |
|---|---|
| adam_text | Contents
1
Introduction
1
1.1
A Basic
Paradigm lor Marketing Problems
2
1.2
Λ
Simple Kxamplc
3
1.3
Benefits and Costs ofthe Bayesian Approach
4
1.4
An
Overview of Methodological Material
and
(lase
Studies
6
1.5
Computing and This Rook
6
Acknowledgements
8
2
Bayesian Essentials
9
2.0
Essential Concepts from Distribution Theory
9
2.1
The Goal of Inference and
Bayes
Theorem
13
2.2
Conditioning and the Likelihood Principle
15
2.3
Prediction and
Bayes
15
2.4
Summarizing the Posterior
16
2.5
Decision Theory, Risk, and the Sampling Properties or
Bayes
Estimators
17
2.6
Identification and Bayesian Inference
19
2.7
Conjugacy, Sufficiency, and Exponential Families
20
2.8
Regression and Multivariate Analysis Examples
21
2.9
Integration and Asymptotic Methods
35
2.10
Importance Sampling
37
2.1 1
Simulation Primer for Bayesian Problems
41
2.12
Simulation from the Posterior of the Multivariate Regression Model
45
3
Markov Chain Monte Carlo Methods
49
3.1
Markov Chain Monte Carlo Methods
50
3.2
A Simple Example: Bivariate Normal Gibbs Sampler
52
3.3
Some Markov Chain Theory
57
3.4
Gibbs Sampler
63
3.5
Gibbs Sampler for the Seemingly Unrelated Regression Model
65
viii CONTENTS
3.6
Conditional Distributions and Directed Graphs
67
3.7
Hierarchical Linear Models
70
3.8
Data Augmentation and
a
Probit Hxample 75
3.9
Mixtures of Normals
79
3.10
Metropolis Algorithms
86
3.11
Metropolis Algorithms Illustrated with the Multinomial Logit
Model
94
3.12
Hybrid Markov Chain Monte Carlo Methods
97
3.13
Diagnostics
99
4
Unit-Level Models and Discrete Demand
103
4.1
Latent Variable Models
104
4.2
Multinomial
Probit
Model
106
4.3
Multivariate
Probit
Model
116
4.4
Demand Theory and Models Involving Discrete Choice
122
5
Hierarchical Models for Heterogeneous Units
129
5.1
Heterogeneity and Priors
130
5.2
Hierarchical Models
132
5.3
Inference for Hierarchical Models
133
5.4
A Hierarchical Multinomial Logit Hxample
136
5.5
Using Mixtures of Normals
142
5.6
Further Klaborations of the Normal Model of Heterogeneity
154
5.7
Diagnostic Checks of the First-Stage Prior
155
5.8
Findings and Influence on Marketing Practice
156
6
Model Choice and Decision Theory
159
6.1
Model Selection
160
6.2
Bayes
Factors in the Conjugate Setting
162
6.3
Asymptotic Methods for Computing
Bayes
Factors
163
6.4
Computing
Bayes
Factors Using Importance Sampling
165
6.5
Bayes
Factors Using MCMC Draws
166
6.6
Bridge Sampling Methods
169
6.7
Posterior Model Probabilities with Unidentified Parameters
170
6.8
Chib s Method
171
6.9
An F.xample of
Bayes
Factor Computation: Diagonal Multinomial
Probit
Models
173
6.10
Marketing Decisions and Bayesian Decision Theory
177
6.11
An FvXample of Bayesian Decision Theory: Valuing Llousehold
Purchase Information
180
7
Simultaneity
185
7.1
A Bayesian Approach to Instrumental Variables
185
CONTENTS ix
7.2
Structural
Models and Hndogeneity/Simiiltaneity 195
7.3 Nonrandom Marketing Mix Variables 200
Case Study
1:
A Choice Model for Packaged Goods: Dealing with
Discrete Quantities and Quantity Discounts
207
Background
207
Model
209
Data
214
Results
219
Discussion
222
R
Implementation
224
Case Study
2:
Modeling Interdependent Consumer Preferences
225
Background
225
Model
226
Data
229
Results
230
Discussion
235
R
Implementation
235
Case Study
3:
Overcoming Scale Usage Heterogeneity
237
Background
237
Model
240
Priors and MCMC Algorithm
244
Data
246
Discussion
251
R
Implementation
252
Case Study
4:
A Choice Model with Conjunctive Screening Rules
253
Background
253
Model
254
Data
255
Results
259
Discussion
264
R
Implementation
266
Case Study
5:
Modeling Consumer Demand for Variety
269
Background
269
Model
270
Data
271
Results
273
Discussion
273
R
Implementation
277
χ
CONTENTS
Appendix
A An Introduction to Hierarchical
Bayes
Modeling in
R
279
A.I Setting Lip the
R
Ivnvironmeni
279
A.
2
The
R
Language
285
A.3 I Iieraichical
Bayes
Modeling An Kxample
303
Appendix
В
A Guide to Installation and Use of
baye
sm
323
B.I Installing bayesm
323
B.2 Using bayesm
323
B.3 Obtaining I Ielp on bayesm
324
B.4 Tips on Using MCMC Methods
327
B.5 Hxtending and Adapting Our Code
327
B.6 Updating bayesm
327
References
335
Index
341
|
| adam_txt |
Contents
1
Introduction
1
1.1
A Basic
Paradigm lor Marketing Problems
2
1.2
Λ
Simple Kxamplc
3
1.3
Benefits and Costs ofthe Bayesian Approach
4
1.4
An
Overview of" Methodological Material
and
(lase
Studies
6
1.5
Computing and This Rook
6
Acknowledgements
8
2
Bayesian Essentials
9
2.0
Essential Concepts from Distribution Theory
9
2.1
The Goal of Inference and
Bayes'
Theorem
13
2.2
Conditioning and the Likelihood Principle
15
2.3
Prediction and
Bayes
15
2.4
Summarizing the Posterior
16
2.5
Decision Theory, Risk, and the Sampling Properties or
Bayes
Estimators
17
2.6
Identification and Bayesian Inference
19
2.7
Conjugacy, Sufficiency, and Exponential Families
20
2.8
Regression and Multivariate Analysis Examples
21
2.9
Integration and Asymptotic Methods
35
2.10
Importance Sampling
37
2.1 1
Simulation Primer for Bayesian Problems
41
2.12
Simulation from the Posterior of the Multivariate Regression Model
45
3
Markov Chain Monte Carlo Methods
49
3.1
Markov Chain Monte Carlo Methods
50
3.2
A Simple Example: Bivariate Normal Gibbs Sampler
52
3.3
Some Markov Chain Theory
57
3.4
Gibbs Sampler
63
3.5
Gibbs Sampler for the Seemingly Unrelated Regression Model
65
viii CONTENTS
3.6
Conditional Distributions and Directed Graphs
67
3.7
Hierarchical Linear Models
70
3.8
Data Augmentation and
a
Probit Hxample 75
3.9
Mixtures of Normals
79
3.10
Metropolis Algorithms
86
3.11
Metropolis Algorithms Illustrated with the Multinomial Logit
Model
94
3.12
Hybrid Markov Chain Monte Carlo Methods
97
3.13
Diagnostics
99
4
Unit-Level Models and Discrete Demand
103
4.1
Latent Variable Models
104
4.2
Multinomial
Probit
Model
106
4.3
Multivariate
Probit
Model
116
4.4
Demand Theory and Models Involving Discrete Choice
122
5
Hierarchical Models for Heterogeneous Units
129
5.1
Heterogeneity and Priors
130
5.2
Hierarchical Models
132
5.3
Inference for Hierarchical Models
133
5.4
A Hierarchical Multinomial Logit Hxample
136
5.5
Using Mixtures of Normals
142
5.6
Further Klaborations of the Normal Model of Heterogeneity
154
5.7
Diagnostic Checks of the First-Stage Prior
155
5.8
Findings and Influence on Marketing Practice
156
6
Model Choice and Decision Theory
159
6.1
Model Selection
160
6.2
Bayes
Factors in the Conjugate Setting
162
6.3
Asymptotic Methods for Computing
Bayes
Factors
163
6.4
Computing
Bayes
Factors Using Importance Sampling
165
6.5
Bayes
Factors Using MCMC Draws
166
6.6
Bridge Sampling Methods
169
6.7
Posterior Model Probabilities with Unidentified Parameters
170
6.8
Chib's Method
171
6.9
An F.xample of
Bayes
Factor Computation: Diagonal Multinomial
Probit
Models
173
6.10
Marketing Decisions and Bayesian Decision Theory
177
6.11
An FvXample of Bayesian Decision Theory: Valuing Llousehold
Purchase Information
180
7
Simultaneity
185
7.1
A Bayesian Approach to Instrumental Variables
185
CONTENTS ix
7.2
Structural
Models and Hndogeneity/Simiiltaneity 195
7.3 Nonrandom Marketing Mix Variables 200
Case Study
1:
A Choice Model for Packaged Goods: Dealing with
Discrete Quantities and Quantity Discounts
207
Background
207
Model
209
Data
214
Results
219
Discussion
222
R
Implementation
224
Case Study
2:
Modeling Interdependent Consumer Preferences
225
Background
225
Model
226
Data
229
Results
230
Discussion
235
R
Implementation
235
Case Study
3:
Overcoming Scale Usage Heterogeneity
237
Background
237
Model
240
Priors and MCMC Algorithm
244
Data
" 246
Discussion
251
R
Implementation
252
Case Study
4:
A Choice Model with Conjunctive Screening Rules
253
Background
253
Model
254
Data
255
Results
259
Discussion
264
R
Implementation
266
Case Study
5:
Modeling Consumer Demand for Variety
269
Background
269
Model
270
Data
271
Results
273
Discussion
273
R
Implementation
277
χ
CONTENTS
Appendix
A An Introduction to Hierarchical
Bayes
Modeling in
R
279
A.I Setting Lip the
R
Ivnvironmeni
279
A.
2
The
R
Language
285
A.3 I Iieraichical
Bayes
Modeling An Kxample
303
Appendix
В
A Guide to Installation and Use of
baye
sm
323
B.I Installing bayesm
323
B.2 Using bayesm
323
B.3 Obtaining I Ielp on bayesm
324
B.4 Tips on Using MCMC Methods
327
B.5 Hxtending and Adapting Our Code
327
B.6 Updating bayesm
327
References
335
Index
341 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Rossi, Peter E. 1955- Allenby, Greg M. 1956- McCulloch, Robert E. |
| author_GND | (DE-588)141279192 (DE-588)170699765 (DE-588)170698955 |
| author_facet | Rossi, Peter E. 1955- Allenby, Greg M. 1956- McCulloch, Robert E. |
| author_role | aut aut aut |
| author_sort | Rossi, Peter E. 1955- |
| author_variant | p e r pe per g m a gm gma r e m re rem |
| building | Verbundindex |
| bvnumber | BV022363903 |
| callnumber-first | H - Social Science |
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| callnumber-subject | HF - Commerce |
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| classification_tum | MAT 624f WIR 803f |
| ctrlnum | (OCoLC)179906097 (DE-599)BVBBV022363903 |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | Repr. with corr. |
| format | Book |
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| id | DE-604.BV022363903 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:04:27Z |
| indexdate | 2024-07-09T20:56:00Z |
| institution | BVB |
| isbn | 0470863676 9780470863671 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015573198 |
| oclc_num | 179906097 |
| open_access_boolean | |
| owner | DE-945 DE-188 DE-M49 DE-BY-TUM DE-92 DE-473 DE-BY-UBG DE-384 |
| owner_facet | DE-945 DE-188 DE-M49 DE-BY-TUM DE-92 DE-473 DE-BY-UBG DE-384 |
| physical | X, 348 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Rossi, Peter E. 1955- Verfasser (DE-588)141279192 aut Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch Repr. with corr. Chichester [u.a.] Wiley 2006 X, 348 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory R Programm (DE-588)4705956-4 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Bayer-Verfahren (DE-588)4210428-2 gnd rswk-swf Marketingforschung (DE-588)4200055-5 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Marketing (DE-588)4037589-4 gnd rswk-swf Marketing (DE-588)4037589-4 s Bayes-Verfahren (DE-588)4204326-8 s DE-188 Marketingforschung (DE-588)4200055-5 s Bayer-Verfahren (DE-588)4210428-2 s R Programm (DE-588)4705956-4 s DE-604 Bayes-Entscheidungstheorie (DE-588)4144220-9 s Mathematisches Modell (DE-588)4114528-8 s 1\p DE-604 Allenby, Greg M. 1956- Verfasser (DE-588)170699765 aut McCulloch, Robert E. Verfasser (DE-588)170698955 aut Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015573198&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 | Rossi, Peter E. 1955- Allenby, Greg M. 1956- McCulloch, Robert E. Bayesian statistics and marketing Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory R Programm (DE-588)4705956-4 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bayer-Verfahren (DE-588)4210428-2 gnd Marketingforschung (DE-588)4200055-5 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Marketing (DE-588)4037589-4 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4204326-8 (DE-588)4114528-8 (DE-588)4210428-2 (DE-588)4200055-5 (DE-588)4144220-9 (DE-588)4037589-4 |
| title | Bayesian statistics and marketing |
| title_auth | Bayesian statistics and marketing |
| title_exact_search | Bayesian statistics and marketing |
| title_exact_search_txtP | Bayesian statistics and marketing |
| title_full | Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch |
| title_fullStr | Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch |
| title_full_unstemmed | Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch |
| title_short | Bayesian statistics and marketing |
| title_sort | bayesian statistics and marketing |
| topic | Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory R Programm (DE-588)4705956-4 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bayer-Verfahren (DE-588)4210428-2 gnd Marketingforschung (DE-588)4200055-5 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Marketing (DE-588)4037589-4 gnd |
| topic_facet | Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory R Programm Bayes-Verfahren Bayer-Verfahren Marketingforschung Bayes-Entscheidungstheorie Marketing |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015573198&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rossipetere bayesianstatisticsandmarketing AT allenbygregm bayesianstatisticsandmarketing AT mccullochroberte bayesianstatisticsandmarketing |