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The Resource Bayesian inference in the social sciences, edited by Ivan Jeliazkov, Department of Economics, University of California, Irvine, California, USA, Xin-She Yang, School of Science and Technology, Middlesex University, London, United Kingdom

Bayesian inference in the social sciences, edited by Ivan Jeliazkov, Department of Economics, University of California, Irvine, California, USA, Xin-She Yang, School of Science and Technology, Middlesex University, London, United Kingdom

Label
Bayesian inference in the social sciences
Title
Bayesian inference in the social sciences
Statement of responsibility
edited by Ivan Jeliazkov, Department of Economics, University of California, Irvine, California, USA, Xin-She Yang, School of Science and Technology, Middlesex University, London, United Kingdom
Contributor
Editor
Subject
Language
eng
Summary
"Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. Particular emphasis is placed on an interdisciplinary coverage, model checking, and modern computational tools such as Markov chain Monte Carlo.The book's broad interdisciplinary coverage provides exposure to recent and trending developments in a diverse, yet closely integrated, set of research topics in the social sciences. This approach facilitates the transmission of new ideas, developments, and methodology from one discipline to another, while at the same time maintaining manageability, coherence, and a clear focus"--
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Illustrations
  • illustrations
  • charts
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Bayesian inference in the social sciences, edited by Ivan Jeliazkov, Department of Economics, University of California, Irvine, California, USA, Xin-She Yang, School of Science and Technology, Middlesex University, London, United Kingdom
Label
Bayesian inference in the social sciences, edited by Ivan Jeliazkov, Department of Economics, University of California, Irvine, California, USA, Xin-She Yang, School of Science and Technology, Middlesex University, London, United Kingdom
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/connectny/detail.action?docID=1833986
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Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type MARC source
rdacontent
Contents
Machine generated contents note: List of Figures iii 1 Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics 1 Zack W. Almquist and Carter T. Butts 1.1 Introduction 2 1.2 Statistical Models for Social Network Data 2 1.3 Dynamic Network Logistic Regression with Vertex Dynamics 11 1.4 Empirical Examples and Simulation Analysis 14 1.5 Discussion 29 1.6 Conclusion 30 2 Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel Analysis 39 Xun Pang 2.1 Introduction: Ethnic Minority Rule and Civil War 40 2.2 EMR: Grievance and Opportunities of Rebellion 41 2.3 Bayesian GLMM-AR(p) Model 42 2.4 Variables, Model and Data 47 2.5 Empirical Results and Interpretation 49 2.6 Civil War: Prediction 54 2.7 Robustness Checking: Alternative Measures of EMR 59 2.8 Conclusion 60 References 62 3 Bayesian Analysis of Treatment Effect Models 67 Mingliang Li and Justin L. Tobias 3.1 Introduction 68 3.2 Linear Treatment Response Models Under Normality 69 3.3 Nonlinear Treatment Response Models 73 3.4 Other Issues and Extensions: Non-Normality, Model Selection and Instrument Imperfection 78 3.5 Illustrative Application 84 3.6 Conclusion 89 4 Bayesian Analysis of Sample Selection Models 95 Martijn van Hasselt 4.1 Introduction 95 4.2 Univariate Selection Models 97 4.3 Multivariate Selection Models 101 4.4 Semiparametric Models 111 4.5 Conclusion 114 References 114 5 Modern Bayesian Factor Analysis 117 Hedibert Freitas Lopes 5.1 Introduction 117 5.2 Normal linear factor analysis 119 5.3 Factor stochastic volatility 125 5.4 Spatial factor analysis 128 5.5 Additional developments 133 5.6 Modern non-Bayesian factor analysis 136 5.7 Final remarks 137 6 Estimation of stochastic volatility models with heavy tails and serial dependence 159 Joshua C.C. Chan and Cody Y.L. Hsiao 6.1 Introduction 159 6.2 Stochastic Volatility Model 160 6.3 Moving Average Stochastic Volatility Model 168 6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173 References 178 7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181 Rui Liu and Ivan Jeliazkov 7.1 Introduction 181 7.2 Methodology 183 7.3 Results 188 7.4 Conclusions 191 Appendix: Data 192 References 192 8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201 Paskalis Glabadanidis 8.1 Introduction 202 8.2 Methodology 204 8.3 Data 205 8.4 Empirical Results 206 8.5 Concluding Remarks 212 9 Stochastic Search For Price Insensitive Consumers 227 Eric Eisenstat 9.1 Introduction 228 9.2 Random utility models in marketing applications 230 9.3 The censored mixing distribution in detail 234 9.4 Reference price models with price thresholds 240 9.5 Conclusion 244 References 245 10 Hierarchical Modeling of Choice Concentration of US Households 249 Karsten T. Hansen, Romana Khan and Vishal Singh 10.1 Introduction 250 10.2 Data Description 252 10.3 Measures of Choice Concentration 252 10.4 Methodology 254 10.5 Results 256 10.6 Interpreting θ 260 10.7 Decomposing the effects of time, number of decisions and concentration preference 263 10.8 Conclusion 265 References 267 11 Approximate Bayesian inference in models defined through estimating equations 269 11.1 Introduction 269 11.2 Examples 271 11.3 Frequentist estimation 273 11.4 Bayesian estimation 276 11.5 Simulating from the posteriors 281 11.6 Asymptotic theory 283 11.7 Bayesian validity 285 11.8 Application 286 11.9 Conclusions 288 12 Reacting to Surprising Seemingly Inappropriate Results 295 Dale J. Poirier 12.1 Introduction 295 12.2 Statistical Framework 296 12.3 Empirical Illustration 300 12.4 Discussion 301 References 301 13 Identification and MCMC estimation of bivariate probit models w ith partial observability 303 Ashish Rajbhandari 13.1 Introduction 303 13.2 Bivariate Probit Model 305 13.3 Identification in a partially observable model 307 13.4 Monte Carlo Simulations 308 13.5 Bayesian Methodology 309 13.6 Application 312 13.7 Conclusion 315 Chapter Appendix 316 References 317 14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321 Kazuhiko Kakamu and Hajime Wago 14.1 Introduction 321 14.2 The Model 323 14.3 Posterior Analysis 325 14.4 Empirical Analysis 326 14.5 Conclusions 330
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{'f': 'http://opac.lib.rpi.edu/record=b4078693'}
Extent
1 online resource (352 pages)
Form of item
online
Isbn
9781118771181
Isbn Type
(e-book)
Media category
computer
Media MARC source
rdamedia
Other physical details
illustrations
Sound
unknown sound
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