Bayesian statistical modelling:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2006
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XI, 573 S graph. Darst. |
| ISBN: | 0470018755 9780470018750 |
Internformat
MARC
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| 100 | 1 | |a Congdon, Peter |d 1949- |e Verfasser |0 (DE-588)170438783 |4 aut | |
| 245 | 1 | 0 | |a Bayesian statistical modelling |c Peter Congdon |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2006 | |
| 300 | |a XI, 573 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 | 7 | |a Besliskunde |2 gtt | |
| 650 | 7 | |a Methode van Bayes |2 gtt | |
| 650 | 4 | |a Statistique bayésienne | |
| 650 | 4 | |a Bayesian statistical decision theory | |
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| 650 | 0 | 7 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |D s |
| 689 | 0 | 1 | |a Software |0 (DE-588)4055382-6 |D s |
| 689 | 0 | |5 DE-604 | |
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| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015403018&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-015403018 | ||
Datensatz im Suchindex
| _version_ | 1804136164352851968 |
|---|---|
| adam_text | Contents
Preface
Chapter
1
Introduction: The Bayesian Method, its Benefits and Implementation
1.1
The
Bayes
approach and its potential advantages
1.2
Expressing prior uncertainty about parameters and Bayesian updating
1.3
MCMC sampling and inferences from posterior densities
1.4
The main MCMC sampling algorithms
1.4.1
Gibbs sampling
1.5
Convergence of MCMC samples
1.6
Predictions from sampling: using the posterior predictive density
1.7
The present book
References
ХШ
1
1
2
5
9
12
14
18
18
19
25
25
28
30
36
38
Direct model averaging by binary and continuous selection indicators
41
Chapter
2
Bayesian Model Choice, Comparison and Checking
2.1
Introduction: the formal approach to
Bayes
model choice and
averaging
Analytic marginal likelihood approximations and the
Bayes
information criterion
Marginal likelihood approximations from the MCMC output
Approximating
Bayes
factors or model probabilities
Joint space search methods
Chapter
3
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
References
The Major Densities and their Application
3.1
Introduction
3.2
Univariate normal with known variance
3.2.1
Testing hypotheses on normal parameters
Predictive model comparison via cross-validation
Predictive fit criteria and posterior predictive model checks
The DIC criterion
Posterior and iteration-specific comparisons of likelihoods and
penalised likelihoods
Monte carlo estimates of model probabilities
43
46
48
50
52
57
63
63
64
66
VI
CONTENTS
3.3
Inference on univariate normal parameters, mean and variance
unknown
3.4
Heavy tailed and skew density alternatives to the normal
3.5
Categorical distributions: binomial and binary data
3.5.1
Simulating controls through historical exposure
3.6
Poisson
distribution for event counts
3.7
The multinomial and dirichlet densities for categorical and
proportional data
3.8
Multivariate continuous data: multivariate normal and
t
densities
3.8.1
Partitioning multivariate priors
3.8.2
The multivariate
/
density
Applications of standard densities: classification rules
Applications of standard densities: multivariate discrimination
3.9
3.10
Exercises
References
Chapter
4
Normal Linear Regression, General Linear Models
and Log-Linear Models
4.1
The context for Bayesian regression methods
4.2
The normal linear regression model
4.2.1
Unknown regression variance
4.3
Normal linear regression: variable and model selection, outlier
detection and error form
4.3.1
Other predictor and model search methods
4.4
Bayesian ridge priors for multicollinearity
4.5
General linear models
4.6
Binary and binomial regression
4.6.1
Priors on regression coefficients
4.6.2
Model checks
4.7
Latent data sampling for binary regression
4.8
Poisson
regression
4.8.1
Poisson
regression for contingency tables
4.8.2
Log-linear model selection
4.9
Multivariate responses
Exercises
References
Chapter
5
Hierarchical Priors for Pooling Strength and Overdispersed
Regression Modelling
5.
1 Hierarchical priors for pooling strength and in general linear
model regression
5.2
Hierarchical priors: conjugate and non-conjugate mixing
5.3
Hierarchical priors for normal data with applications in
meta-analysis
5.3.1
Prior for second-stage variance
69
71
74
76
79
82
85
87
88
91
98
100
102
109
109
111
112
116
118
121
123
123
124
126
129
132
134
139
140
143
146
151
151
152
153
155
CONTENTS
VU
5.4
Pooling strength under exchangeable models for
poisson
outcomes
5.4.1
Hierarchical prior choices
5.4.2
Parameter sampling
5.5
Combining information for binomial outcomes
5.6
Random effects regression for overdispersed count and
binomial data
5.7
Overdispersed normal regression: the scale-mixture student
t
model
5.8
The normal meta-analysis model allowing for heterogeneity in
study design or patient risk
5.9
Hierarchical priors for multinomial data
5.9.1
Histogram smoothing
Exercises
References
Chapter
6
Discrete Mixture Priors
6.1
Introduction: the relevance and applicability of discrete mixtures
6.2
Discrete mixtures of parametric densities
6.2.1
Model choice
6.3
Identifiability constraints
6.4
Hurdle and zero-inflated models for discrete data
6.5
Regression mixtures for heterogeneous
subpopulations
6.6
Discrete mixtures combined with parametric random effects
6.7
Non-parametric mixture modelling via dirichlet process priors
6.8
Other non-parametric priors
Exercises
References
Chapter
7
Multinomial and Ordinal Regression Models
7.1
Introduction: applications with categoric and ordinal data
7.2
Multinomial logit choice models
7.3
The multinomial
probit
representation of interdependent choices
7.4
Mixed multinomial logit models
7.5
Individual level ordinal regression
7.6
Scores for ordered factors in contingency tables
Exercises
References
Chapter
8
Time Series Models
8.1
Introduction: alternative approaches to time series models
8.2
Autoregressive
models in the observations
8.2.1
Priors on
autoregressive
coefficients
8.2.2
Initial conditions as latent data
8.3
Trend stationarity in the
AR 1
model
8.4
Autoregressive
moving average models
157
158
159
162
165
169
173
176
177
179
183
187
187
188
190
191
195
197
200
201
207
212
216
219
219
221
224
228
230
235
237
238
241
241
242
244
246
248
250
Vlil
CONTENTS
8.5
Autoregressive
errors
253
8.6
Multivariate
series
255
8.7
Time series models for discrete outcomes
257
8.7.1
Observation-driven autodependence
257
8.7.2
INAR models
258
8.7.3
Error autocorrelation
259
8.8
Dynamic linear models and time varying coefficients
261
8.8.1
Some common forms of DLM
264
8.8.2
Priors for time-specific variances or interventions
267
8.8.3
Nonlinear and non-Gaussian state-space models
268
8.9
Models for variance evolution
273
8.9.1
ARCH and GARCH models
274
8.9.2
Stochastic volatility models
275
8.10
Modelling structural shifts and outliers
277
8.10.1
Markov mixtures and transition functions
279
8.11
Other nonlinear models
282
Exercises
285
References
288
Chapter
9
Modelling Spatial Dependencies
297
9.1
Introduction: implications of spatial dependence
297
9.2
Discrete space regressions for metric data
298
9.3
Discrete spatial regression with structured and unstructured
random effects
303
9.3.1
Proper CAR priors
306
9.4
Moving average priors
311
9.5
Multivariate spatial priors and spatially varying regression effects
313
9.6
Robust models for discontinuities and non-standard errors
317
9.7
Continuous space modelling in regression and interpolation
321
Exercises
325
References
329
Chapter
10
Nonlinear and Nonparametric Regression
333
10.1
Approaches to modelling nonlinearity
333
10.2
Nonlinear metric data models with known functional form
335
10.3
Box
-Сох
transformations and fractional polynomials
338
10.4
Nonlinear regression through spline and radial basis functions
342
10.4.1
Shrinkage models for spline coefficients
345
10.4.2
Modelling interaction effects
346
10.5
Application of state-space priors in general additive
nonparametric regression
350
10.5.1
Continuous predictor space prior
351
10.5.2
Discrete predictor space priors
353
Exercises
359
References
362
CONTENTS
IX
Chapter
11
Multilevel and Panel Data Models
11.1
Introduction: nested data structures
11.2
Multilevel structures
11.2.1
The multilevel normal linear model
11.2.2
General linear mixed models for discrete outcomes
11.2.3
Multinomial and ordinal multilevel models
11.2.4
Robustness regarding cluster effects
11.2.5
Conjugate approaches for discrete data
11.3
Heteroscedasticity in multilevel models
11.4
Random effects for crossed factors
11.5
Panel data models: the normal mixed model and extensions
11.5.1
Autocorrelated errors
11.5.2 Autoregression
in
y
11.6
Models for panel discrete (binary, count and categorical)
observations
11.6.1
Binary panel data
11.6.2
Repeated counts
11.6.3
Panel categorical data
11.7
Growth curve models
11.8
Dynamic models for longitudinal data: pooling strength over
units and times
11.9
Area ape and
spatiotemporal
models
11.9.1
Age-period data
11.9.2
Area-time data
11.9.3
Age-area-period data
11.9.4
Interaction priors
Exercises
References
Chapter
12
Chapter
13
Latent Variable and Structural Equation Models
for Multivariate Data
12.1
Introduction: latent traits and latent classes
12.2
Factor analysis and
SEMS
for continuous data
12.2.1
Identifiability constraints in latent trait (factor
analysis) models
12.3
Latent class models
12.3.1
Local dependence
12.4
Factor analysis and
SEMS
for multivariate discrete data
12.5
Nonlinear factor models
Exercises
References
Survival and Event History Analysis
13.1
Introduction
13.2
Parametric survival analysis in continuous time
367
367
369
369
370
372
373
374
379
381
387
390
391
393
393
395
397
400
403
407
408
409
409
410
413
418
425
425
427
429
433
437
441
447
450
452
4S7
457
458
CONTENTS
13.2.1
Censored
observations
459
13.2.2
Forms of parametric hazard and survival curves
460
13.2.3
Modelling covariate impacts and time dependence in
the hazard rate
461
13.3
Accelerated hazard parametric models
464
13.4
Counting process models
466
13.5
Semiparametric hazard models
469
13.5.1
Priors for the baseline hazard
470
13.5.2
Gamma process prior on cumulative hazard
472
13.6
Competing risk-continuous time models
475
13.7
Variations in proneness: models for frailty
477
13.8
Discrete time survival models
482
Exercises
486
References
487
Chapter
14
Missing Data Models
493
14.1
Introduction: types of missingness
493
14.2
Selection and pattern mixture models for the joint
data-missingness density
494
14.3
Shared random effect and common factor models
498
14.4
Missing predictor data
500
14.5
Multiple imputation
503
14.6
Categorical response data with possible non-random
missingness: hierarchical and regression models
506
14.6.1
Hierarchical models for response and non-response
by strata
506
14.6.2
Regression frameworks
510
14.7
Missingness with mixtures of continuous and categorical
data
516
14.8
Missing cells in contingency tables
518
14.8.1
Ecological inference
519
Exercises
526
References
529
Chapter
15
Measurement Error, Seemingly Unrelated Regressions, and
Simultaneous Equations
533
15.1
Introduction
533
15.2
Measurement error in both predictors and response in normal
linear regression
533
15.2.1
Prior information on X or its density
535
15.2.2
Measurement error in general linear models
537
15.3
Misclassification of categorical variables
541
15.4
Simultaneous equations and instruments for endogenous
variables
546
CONTENTS
Xl
15.5
Endogenous regression involving
discrete
variables
550
Exercises
554
References
556
Appendix
1
A Brief Guide to Using WINBUGS
561
A
1.1
Procedure for compiling and running programs
561
A
1.2
Generating simulated data
562
A1.3 Other advice
563
Index
565
Bayesian methods combine the evidence from the data at hand with previous quantitative
knowledge to analyse practical problems in a wide range of areas. The calculations were
previously complex, but it is now possible to routinely apply Bayesian methods due to
advances in computing technology and the use of new sampling methods for estimating
parameters. Such developments together with the availability of freeware such as
WINBUGS and
R
have facilitated a rapid growth in the use of Bayesian methods, allowing
their application in many scientific disciplines, including applied statistics, public health
research, medical science, the social sciences and economics.
Following the success of the first edition, this reworked and updated book provides an
accessible approach to Bayesian computing and analysis, with an emphasis on the
principles of prior selection, identification and the interpretation of real data sets.
The second edition:
•
Provides an integrated presentation of theory, examples, applications and computer
algorithms.
•
Discusses the role of Markov Chain Monte Carlo methods in computing and
estimation.
•
Includes a wide range of interdisciplinary applications, and a large selection of worked
examples from the health and social sciences.
•
Features a comprehensive range of methodologies and modelling techniques, and
examines model fitting in practice using Bayesian principles.
•
Provides exercises designed to help reinforce the reader s knowledge and a
supplementary website containing data sets and relevant programs.
Bayesian Statistical Modelling is ¡deal for researchers in applied statistics, medical
science, public health and the social sciences, who will benefit greatly from the examples
and applications featured. The book will also appeal to graduate students of applied
statistics, data analysis and Bayesian methods, and will provide a great source of
reference for both researchers and students.
|
| adam_txt |
Contents
Preface
Chapter
1
Introduction: The Bayesian Method, its Benefits and Implementation
1.1
The
Bayes
approach and its potential advantages
1.2
Expressing prior uncertainty about parameters and Bayesian updating
1.3
MCMC sampling and inferences from posterior densities
1.4
The main MCMC sampling algorithms
1.4.1
Gibbs sampling
1.5
Convergence of MCMC samples
1.6
Predictions from sampling: using the posterior predictive density
1.7
The present book
References
ХШ
1
1
2
5
9
12
14
18
18
19
25
25
28
30
36
38
Direct model averaging by binary and continuous selection indicators
41
Chapter
2
Bayesian Model Choice, Comparison and Checking
2.1
Introduction: the formal approach to
Bayes
model choice and
averaging
Analytic marginal likelihood approximations and the
Bayes
information criterion
Marginal likelihood approximations from the MCMC output
Approximating
Bayes
factors or model probabilities
Joint space search methods
Chapter
3
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
References
The Major Densities and their Application
3.1
Introduction
3.2
Univariate normal with known variance
3.2.1
Testing hypotheses on normal parameters
Predictive model comparison via cross-validation
Predictive fit criteria and posterior predictive model checks
The DIC criterion
Posterior and iteration-specific comparisons of likelihoods and
penalised likelihoods
Monte carlo estimates of model probabilities
43
46
48
50
52
57
63
63
64
66
VI
CONTENTS
3.3
Inference on univariate normal parameters, mean and variance
unknown
3.4
Heavy tailed and skew density alternatives to the normal
3.5
Categorical distributions: binomial and binary data
3.5.1
Simulating controls through historical exposure
3.6
Poisson
distribution for event counts
3.7
The multinomial and dirichlet densities for categorical and
proportional data
3.8
Multivariate continuous data: multivariate normal and
t
densities
3.8.1
Partitioning multivariate priors
3.8.2
The multivariate
/
density
Applications of standard densities: classification rules
Applications of standard densities: multivariate discrimination
3.9
3.10
Exercises
References
Chapter
4
Normal Linear Regression, General Linear Models
and Log-Linear Models
4.1
The context for Bayesian regression methods
4.2
The normal linear regression model
4.2.1
Unknown regression variance
4.3
Normal linear regression: variable and model selection, outlier
detection and error form
4.3.1
Other predictor and model search methods
4.4
Bayesian ridge priors for multicollinearity
4.5
General linear models
4.6
Binary and binomial regression
4.6.1
Priors on regression coefficients
4.6.2
Model checks
4.7
Latent data sampling for binary regression
4.8
Poisson
regression
4.8.1
Poisson
regression for contingency tables
4.8.2
Log-linear model selection
4.9
Multivariate responses
Exercises
References
Chapter
5
Hierarchical Priors for Pooling Strength and Overdispersed
Regression Modelling
5.
1 Hierarchical priors for pooling strength and in general linear
model regression
5.2
Hierarchical priors: conjugate and non-conjugate mixing
5.3
Hierarchical priors for normal data with applications in
meta-analysis
5.3.1
Prior for second-stage variance
69
71
74
76
79
82
85
87
88
91
98
100
102
109
109
111
112
116
118
121
123
123
124
126
129
132
134
139
140
143
146
151
151
152
153
155
CONTENTS
VU
5.4
Pooling strength under exchangeable models for
poisson
outcomes
5.4.1
Hierarchical prior choices
5.4.2
Parameter sampling
5.5
Combining information for binomial outcomes
5.6
Random effects regression for overdispersed count and
binomial data
5.7
Overdispersed normal regression: the scale-mixture student
t
model
5.8
The normal meta-analysis model allowing for heterogeneity in
study design or patient risk
5.9
Hierarchical priors for multinomial data
5.9.1
Histogram smoothing
Exercises
References
Chapter
6
Discrete Mixture Priors
6.1
Introduction: the relevance and applicability of discrete mixtures
6.2
Discrete mixtures of parametric densities
6.2.1
Model choice
6.3
Identifiability constraints
6.4
Hurdle and zero-inflated models for discrete data
6.5
Regression mixtures for heterogeneous
subpopulations
6.6
Discrete mixtures combined with parametric random effects
6.7
Non-parametric mixture modelling via dirichlet process priors
6.8
Other non-parametric priors
Exercises
References
Chapter
7
Multinomial and Ordinal Regression Models
7.1
Introduction: applications with categoric and ordinal data
7.2
Multinomial logit choice models
7.3
The multinomial
probit
representation of interdependent choices
7.4
Mixed multinomial logit models
7.5
Individual level ordinal regression
7.6
Scores for ordered factors in contingency tables
Exercises
References
Chapter
8
Time Series Models
8.1
Introduction: alternative approaches to time series models
8.2
Autoregressive
models in the observations
8.2.1
Priors on
autoregressive
coefficients
8.2.2
Initial conditions as latent data
8.3
Trend stationarity in the
AR 1
model
8.4
Autoregressive
moving average models
157
158
159
162
165
169
173
176
177
179
183
187
187
188
190
191
195
197
200
201
207
212
216
219
219
221
224
228
230
235
237
238
241
241
242
244
246
248
250
Vlil
CONTENTS
8.5
Autoregressive
errors
253
8.6
Multivariate
series
255
8.7
Time series models for discrete outcomes
257
8.7.1
Observation-driven autodependence
257
8.7.2
INAR models
258
8.7.3
Error autocorrelation
259
8.8
Dynamic linear models and time varying coefficients
261
8.8.1
Some common forms of DLM
264
8.8.2
Priors for time-specific variances or interventions
267
8.8.3
Nonlinear and non-Gaussian state-space models
268
8.9
Models for variance evolution
273
8.9.1
ARCH and GARCH models
274
8.9.2
Stochastic volatility models
275
8.10
Modelling structural shifts and outliers
277
8.10.1
Markov mixtures and transition functions
279
8.11
Other nonlinear models
282
Exercises
285
References
288
Chapter
9
Modelling Spatial Dependencies
297
9.1
Introduction: implications of spatial dependence
297
9.2
Discrete space regressions for metric data
298
9.3
Discrete spatial regression with structured and unstructured
random effects
303
9.3.1
Proper CAR priors
306
9.4
Moving average priors
311
9.5
Multivariate spatial priors and spatially varying regression effects
313
9.6
Robust models for discontinuities and non-standard errors
317
9.7
Continuous space modelling in regression and interpolation
321
Exercises
325
References
329
Chapter
10
Nonlinear and Nonparametric Regression
333
10.1
Approaches to modelling nonlinearity
333
10.2
Nonlinear metric data models with known functional form
335
10.3
Box
-Сох
transformations and fractional polynomials
338
10.4
Nonlinear regression through spline and radial basis functions
342
10.4.1
Shrinkage models for spline coefficients
345
10.4.2
Modelling interaction effects
346
10.5
Application of state-space priors in general additive
nonparametric regression
350
10.5.1
Continuous predictor space prior
351
10.5.2
Discrete predictor space priors
353
Exercises
359
References
362
CONTENTS
IX
Chapter
11
Multilevel and Panel Data Models
11.1
Introduction: nested data structures
11.2
Multilevel structures
11.2.1
The multilevel normal linear model
11.2.2
General linear mixed models for discrete outcomes
11.2.3
Multinomial and ordinal multilevel models
11.2.4
Robustness regarding cluster effects
11.2.5
Conjugate approaches for discrete data
11.3
Heteroscedasticity in multilevel models
11.4
Random effects for crossed factors
11.5
Panel data models: the normal mixed model and extensions
11.5.1
Autocorrelated errors
11.5.2 Autoregression
in
y
11.6
Models for panel discrete (binary, count and categorical)
observations
11.6.1
Binary panel data
11.6.2
Repeated counts
11.6.3
Panel categorical data
11.7
Growth curve models
11.8
Dynamic models for longitudinal data: pooling strength over
units and times
11.9
Area ape and
spatiotemporal
models
11.9.1
Age-period data
11.9.2
Area-time data
11.9.3
Age-area-period data
11.9.4
Interaction priors
Exercises
References
Chapter
12
Chapter
13
Latent Variable and Structural Equation Models
for Multivariate Data
12.1
Introduction: latent traits and latent classes
12.2
Factor analysis and
SEMS
for continuous data
12.2.1
Identifiability constraints in latent trait (factor
analysis) models
12.3
Latent class models
12.3.1
Local dependence
12.4
Factor analysis and
SEMS
for multivariate discrete data
12.5
Nonlinear factor models
Exercises
References
Survival and Event History Analysis
13.1
Introduction
13.2
Parametric survival analysis in continuous time
367
367
369
369
370
372
373
374
379
381
387
390
391
393
393
395
397
400
403
407
408
409
409
410
413
418
425
425
427
429
433
437
441
447
450
452
4S7
457
458
CONTENTS
13.2.1
Censored
observations
459
13.2.2
Forms of parametric hazard and survival curves
460
13.2.3
Modelling covariate impacts and time dependence in
the hazard rate
461
13.3
Accelerated hazard parametric models
464
13.4
Counting process models
466
13.5
Semiparametric hazard models
469
13.5.1
Priors for the baseline hazard
470
13.5.2
Gamma process prior on cumulative hazard
472
13.6
Competing risk-continuous time models
475
13.7
Variations in proneness: models for frailty
477
13.8
Discrete time survival models
482
Exercises
486
References
487
Chapter
14
Missing Data Models
493
14.1
Introduction: types of missingness
493
14.2
Selection and pattern mixture models for the joint
data-missingness density
494
14.3
Shared random effect and common factor models
498
14.4
Missing predictor data
500
14.5
Multiple imputation
503
14.6
Categorical response data with possible non-random
missingness: hierarchical and regression models
506
14.6.1
Hierarchical models for response and non-response
by strata
506
14.6.2
Regression frameworks
510
14.7
Missingness with mixtures of continuous and categorical
data
516
14.8
Missing cells in contingency tables
518
14.8.1
Ecological inference
519
Exercises
526
References
529
Chapter
15
Measurement Error, Seemingly Unrelated Regressions, and
Simultaneous Equations
533
15.1
Introduction
533
15.2
Measurement error in both predictors and response in normal
linear regression
533
15.2.1
Prior information on X or its density
535
15.2.2
Measurement error in general linear models
537
15.3
Misclassification of categorical variables
541
15.4
Simultaneous equations and instruments for endogenous
variables
546
CONTENTS
Xl
15.5
Endogenous regression involving
discrete
variables
550
Exercises
554
References
556
Appendix
1
A Brief Guide to Using WINBUGS
561
A
1.1
Procedure for compiling and running programs
561
A
1.2
Generating simulated data
562
A1.3 Other advice
563
Index
565
Bayesian methods combine the evidence from the data at hand with previous quantitative
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previously complex, but it is now possible to routinely apply Bayesian methods due to
advances in computing technology and the use of new sampling methods for estimating
parameters. Such developments together with the availability of freeware such as
WINBUGS and
R
have facilitated a rapid growth in the use of Bayesian methods, allowing
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Following the success of the first edition, this reworked and updated book provides an
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•
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•
Discusses the role of Markov Chain Monte Carlo methods in computing and
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•
Includes a wide range of interdisciplinary applications, and a large selection of worked
examples from the health and social sciences.
•
Features a comprehensive range of methodologies and modelling techniques, and
examines model fitting in practice using Bayesian principles.
•
Provides exercises designed to help reinforce the reader's knowledge and a
supplementary website containing data sets and relevant programs.
Bayesian Statistical Modelling is ¡deal for researchers in applied statistics, medical
science, public health and the social sciences, who will benefit greatly from the examples
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reference for both researchers and students. |
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| author | Congdon, Peter 1949- |
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| classification_tum | MAT 622f |
| ctrlnum | (OCoLC)70673258 (DE-599)BVBBV022191456 |
| dewey-full | 519.5/42 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/42 |
| dewey-search | 519.5/42 |
| dewey-sort | 3519.5 242 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| illustrated | Illustrated |
| index_date | 2024-07-02T16:21:22Z |
| indexdate | 2024-07-09T20:52:03Z |
| institution | BVB |
| isbn | 0470018755 9780470018750 |
| language | English |
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| physical | XI, 573 S graph. Darst. |
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| series2 | Wiley series in probability and statistics |
| spelling | Congdon, Peter 1949- Verfasser (DE-588)170438783 aut Bayesian statistical modelling Peter Congdon 2. ed. Chichester [u.a.] Wiley 2006 XI, 573 S graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Besliskunde gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Software (DE-588)4055382-6 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 s Software (DE-588)4055382-6 s DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015403018&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=015403018&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Congdon, Peter 1949- Bayesian statistical modelling Besliskunde gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Software (DE-588)4055382-6 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| subject_GND | (DE-588)4055382-6 (DE-588)4204326-8 |
| title | Bayesian statistical modelling |
| title_auth | Bayesian statistical modelling |
| title_exact_search | Bayesian statistical modelling |
| title_exact_search_txtP | Bayesian statistical modelling |
| title_full | Bayesian statistical modelling Peter Congdon |
| title_fullStr | Bayesian statistical modelling Peter Congdon |
| title_full_unstemmed | Bayesian statistical modelling Peter Congdon |
| title_short | Bayesian statistical modelling |
| title_sort | bayesian statistical modelling |
| topic | Besliskunde gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Software (DE-588)4055382-6 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Besliskunde Methode van Bayes Statistique bayésienne Bayesian statistical decision theory Software Bayes-Verfahren |
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