Bayesian statistics and marketing:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2007
|
| Ausgabe: | Reprint. |
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | X, 348 S. graph. Darst. |
Internformat
MARC
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| 100 | 1 | |a Rossi, Peter E. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Bayesian statistics and marketing |c Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch |
| 250 | |a Reprint. | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2007 | |
| 300 | |a X, 348 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 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 | |
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Datensatz im Suchindex
| _version_ | 1804137295953002496 |
|---|---|
| adam_text | Contents
1
Introduction
1
1.1
A
Basic
Paradigm for
Marketing Problems 2
1.2
A Simple Example
3
1.3
Benefits and Costs of the Bayesian Approach
4
1.4
An Overview of Methodological Material
and Case Studies
6
1.5
Computing and This Book
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 of
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
2.11
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
Example
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
Mul
tivariate
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 Example
136
5.5
Using Mixtures of Normals
142
5.6
Further Elaborations 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 Example of
Bayes
Factor Computation: Diagonal Multinomial
Probit
Models
173
6.10
Marketing Decisions and Bayesian Decision Theory
177
6.11
An Example of Bayesian Decision Theory: Valuing Household
Purchase Information
180
7
Simultaneity
185
7.1
A Bayesian Approach to Instrumental Variables
185
CONTENTS ¡x
7.2
Structural
Models
and Endogeneity/Simultaneity
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.
1
Setting Up the
R
Environment
279
A.
2
The
R
Language
285
A.3 Hierarchical
Bayes
Modeling
-
An Example
303
Appendix
В
A Guide to Installation and Use of bayesm
323
B.I Installing bayesm
323
B.2 Using bayesm
323
B.3 Obtaining Help on bayesm
324
B.4 Tips on Using MCMC Methods
327
B.5 Extending and Adapting Our Code
327
B.6 Updating bayesm
327
References
335
Index
341
Bayesian Statistics and Marketing
Peter E. Rossi Graduate School of Business, University of Chicago, USA
Greg M. Allenby Fisher College of Business, Ohio State University, USA
Robert McCulloch Graduate School of Business, University of Chicago, USA
The past decade has seen a dramatic increase in the use of Bayesian
methods in marketing due, in part, to computational and modelling
breakthroughs, making its implementation ideal for many marketing
problems. Bayesian analyses can now be conducted over a wide range of
marketing problems, from new product introduction to pricing, and with a
wide variety of different data sources.
Bayesian Statistics and Marketing describes the basic advantages of the
Bayesian approach, detailing the nature of the computational revolution.
Examples contained include household and consumer panel data on product
purchases and survey data, demand models based on micro-economic
theory and random effect models used to pool data across respondents. The
book also discusses the theory and practical use of MCMC methods.
Key Features:
•
Unified treatment of Bayesian methods in marketing, with common
notation and algorithms for estimating the models.
•
Self-contained introduction to Bayesian methods.
•
Case studies drawn from the authors recent research to illustrate how
Bayesian methods can be extended to apply to many important
marketing problems.
•
Accompanied by an
R
package, bayesm, which implements all of the
models and methods in the book and includes many
datasets.
In addition the book s website hosts
datasets
and
R
code for the case
studies.
Bayesian Statistics and Marketing provides a platform for researchers in
marketing to analyse their data with state-of-the-art methods and develop new
models of consumer behaviour. It provides a unified reference for cutting-
edge marketing researchers, as well as an invaluable guide to this growing
area for both graduate students and professors, alike.
The cover art for this book contains two graphic images. The foreground image illustrates the
phenomenon of shrinkage of
Bayes
estimates of regression parameters. The background
image shows a posterior distribution constructed from MCMC draws (the magenta histogram)
along with the prior (green line) and a normal approximation (black density curve).
|
| adam_txt |
Contents
1
Introduction
1
1.1
A
Basic
Paradigm for
Marketing Problems 2
1.2
A Simple Example
3
1.3
Benefits and Costs of the Bayesian Approach
4
1.4
An Overview of Methodological Material
and Case Studies
6
1.5
Computing and This Book
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 of
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
2.11
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
Example
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
Mul
tivariate
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 Example
136
5.5
Using Mixtures of Normals
142
5.6
Further Elaborations 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 Example of
Bayes
Factor Computation: Diagonal Multinomial
Probit
Models
173
6.10
Marketing Decisions and Bayesian Decision Theory
177
6.11
An Example of Bayesian Decision Theory: Valuing Household
Purchase Information
180
7
Simultaneity
185
7.1
A Bayesian Approach to Instrumental Variables
185
CONTENTS ¡x
7.2
Structural
Models
and Endogeneity/Simultaneity
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.
1
Setting Up the
R
Environment
279
A.
2
The
R
Language
285
A.3 Hierarchical
Bayes
Modeling
-
An Example
303
Appendix
В
A Guide to Installation and Use of bayesm
323
B.I Installing bayesm
323
B.2 Using bayesm
323
B.3 Obtaining Help on bayesm
324
B.4 Tips on Using MCMC Methods
327
B.5 Extending and Adapting Our Code
327
B.6 Updating bayesm
327
References
335
Index
341
Bayesian Statistics and Marketing
Peter E. Rossi Graduate School of Business, University of Chicago, USA
Greg M. Allenby Fisher College of Business, Ohio State University, USA
Robert McCulloch Graduate School of Business, University of Chicago, USA
The past decade has seen a dramatic increase in the use of Bayesian
methods in marketing due, in part, to computational and modelling
breakthroughs, making its implementation ideal for many marketing
problems. Bayesian analyses can now be conducted over a wide range of
marketing problems, from new product introduction to pricing, and with a
wide variety of different data sources.
Bayesian Statistics and Marketing describes the basic advantages of the
Bayesian approach, detailing the nature of the computational revolution.
Examples contained include household and consumer panel data on product
purchases and survey data, demand models based on micro-economic
theory and random effect models used to pool data across respondents. The
book also discusses the theory and practical use of MCMC methods.
Key Features:
•
Unified treatment of Bayesian methods in marketing, with common
notation and algorithms for estimating the models.
•
Self-contained introduction to Bayesian methods.
•
Case studies drawn from the authors' recent research to illustrate how
Bayesian methods can be extended to apply to many important
marketing problems.
•
Accompanied by an
R
package, bayesm, which implements all of the
models and methods in the book and includes many
datasets.
In addition the book's website hosts
datasets
and
R
code for the case
studies.
Bayesian Statistics and Marketing provides a platform for researchers in
marketing to analyse their data with state-of-the-art methods and develop new
models of consumer behaviour. It provides a unified reference for cutting-
edge marketing researchers, as well as an invaluable guide to this growing
area for both graduate students and professors, alike.
The cover art for this book contains two graphic images. The foreground image illustrates the
phenomenon of shrinkage of
Bayes
estimates of regression parameters. The background
image shows a posterior distribution constructed from MCMC draws (the magenta histogram)
along with the prior (green line) and a normal approximation (black density curve). |
| any_adam_object | 1 |
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| author | Rossi, Peter E. Allenby, Greg M. McCulloch, Robert |
| author_facet | Rossi, Peter E. Allenby, Greg M. McCulloch, Robert |
| author_role | aut aut aut |
| author_sort | Rossi, Peter E. |
| author_variant | p e r pe per g m a gm gma r m rm |
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| ctrlnum | (OCoLC)254180053 (DE-599)BVBBV023059222 |
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| edition | Reprint. |
| format | Book |
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| id | DE-604.BV023059222 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:28:06Z |
| indexdate | 2024-07-09T21:10:02Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016262481 |
| oclc_num | 254180053 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-355 DE-BY-UBR |
| owner_facet | DE-91 DE-BY-TUM DE-355 DE-BY-UBR |
| physical | X, 348 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Rossi, Peter E. Verfasser aut Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch Reprint. Chichester [u.a.] Wiley 2007 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 Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Bayer-Verfahren (DE-588)4210428-2 gnd rswk-swf Marketingforschung (DE-588)4200055-5 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Marketing (DE-588)4037589-4 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Marketingforschung (DE-588)4200055-5 s Marketing (DE-588)4037589-4 s Bayes-Entscheidungstheorie (DE-588)4144220-9 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Bayer-Verfahren (DE-588)4210428-2 s R Programm (DE-588)4705956-4 s 1\p DE-604 Bayes-Verfahren (DE-588)4204326-8 s 2\p DE-604 3\p DE-604 Allenby, Greg M. Verfasser aut McCulloch, Robert Verfasser aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016262481&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=016262481&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 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Rossi, Peter E. Allenby, Greg M. McCulloch, Robert Bayesian statistics and marketing Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Bayer-Verfahren (DE-588)4210428-2 gnd Marketingforschung (DE-588)4200055-5 gnd Mathematisches Modell (DE-588)4114528-8 gnd Marketing (DE-588)4037589-4 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4204326-8 (DE-588)4144220-9 (DE-588)4210428-2 (DE-588)4200055-5 (DE-588)4114528-8 (DE-588)4037589-4 (DE-588)4705956-4 |
| 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 Bayes-Verfahren (DE-588)4204326-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Bayer-Verfahren (DE-588)4210428-2 gnd Marketingforschung (DE-588)4200055-5 gnd Mathematisches Modell (DE-588)4114528-8 gnd Marketing (DE-588)4037589-4 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory Bayes-Verfahren Bayes-Entscheidungstheorie Bayer-Verfahren Marketingforschung Marketing R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016262481&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016262481&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rossipetere bayesianstatisticsandmarketing AT allenbygregm bayesianstatisticsandmarketing AT mccullochrobert bayesianstatisticsandmarketing |