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008			171212s2014 nju ob 001 0 eng
010			_a2014-014354
020			_a9781118771129 _q(epub)
020			_a1118771125 _q(epub)
020			_a9781118771181 _q(pdf)
020			_a1118771184 _q(pdf)
020			_a9781118771051
020			_a1118771052
020			_z9781118771211 _q(hardback)
020			_z1118771214
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035			_a(OCoLC)876466907 _z(OCoLC)961540002 _z(OCoLC)962689936
035			_a(OCoLC)ocn876466907
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040			_aDLC _beng _erda _epn _cDLC _dOCLCF _dNST _dEBLCP _dDG1 _dE7B _dYDXCP _dCOO _dRECBK _dDEBSZ _dOCLCQ _dDEBBG
042			_apcc
049			_aMAIN
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072		7	_aMAT _x003000 _2bisacsh
072		7	_aMAT _x029000 _2bisacsh
082	0	0	_a519.5/42 _223
245	0	0	_aBayesian inference in the social sciences / _cedited 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.
264		1	_aHoboken, New Jersey : _bJohn Wiley & Sons, Inc, _c2014.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
505	8		_aMachine 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 & theta; 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.
520			_a'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'-- _cProvided by publisher.
588	0		_aPrint version record and CIP data.
650		0	_aSocial sciences _xStatistical methods. _960875
650		0	_aBayesian statistical decision theory.
650		7	_aMATHEMATICS _xProbability & Statistics _xBayesian Analysis. _2bisacsh
650		7	_aSOCIAL SCIENCE _xStatistics. _2bisacsh
650		7	_aBUSINESS & ECONOMICS _xEconometrics. _2bisacsh
650		7	_aBayesian statistical decision theory. _2fast _0(OCoLC)fst00829019
650		7	_aSocial sciences _xStatistical methods. _2fast _0(OCoLC)fst01122983 _960875
655		4	_aElectronic books.
700	1		_aJeliazkov, Ivan, _d1973-
700	1		_aYang, Xin-She. _957771
773	0		_tWiley e-books
776	0	8	_iPrint version: _tBayesian inference in the social sciences. _dHoboken, New Jersey : Wiley, 2014 _z9781118771211 _w(DLC) 2014011437
856	4	0	_uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url=http://onlinelibrary.wiley.com/book/10.1002/9781118771051 _zWiley Online Library
907			_a.b16543361 _b2022-11-04 _c2019-11-12
942			_n0
914			_avtls003629267
998			_ae _b2017-12-12 _cm _dz _feng _gnju _y0 _z.b16543361
999			_c689279 _d689279