bayes.bib

% Encoding: UTF-8

@Book{JohnsonAlbert1999a,
  author    = {Johnson, Valen E. and Albert, James H.},
  title     = {Ordinal Data Modeling},
  year      = {1999},
  publisher = {Springer-Verlag},
  doi       = {10.1007/b98832},
  timestamp = {2017-05-30},
}

@Article{Dorn1954a,
  author    = {Harold F. Dorn},
  title     = {The Relationship of Cancer of the Lung and the Use of Tobacco},
  journal   = {The American Statistician},
  year      = {1954},
  volume    = {8},
  number    = {5},
  pages     = {7-13},
  url       = {http://www.jstor.org/stable/2681545},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  timestamp = {2017-05-31},
}

@Article{Jackman2004a,
  author       = {Jackman, Simon},
  title        = {{Bayesian} Analysis for Political Research},
  journaltitle = {Annual Review of Political Science},
  date         = {2004-05},
  volume       = {7},
  number       = {1},
  pages        = {483--505},
  doi          = {10.1146/annurev.polisci.7.012003.104706},
  publisher    = {Annual Reviews},
  timestamp    = {2017-05-30},
}

@Article{Bliss1935a,
  author    = {Bliss, C. I.},
  title     = {The Calculation of the Dosage-Mortality Curve},
  journal   = {Annals of Applied Biology},
  year      = {1935},
  volume    = {22},
  number    = {1},
  month     = {2},
  pages     = {134--167},
  doi       = {10.1111/j.1744-7348.1935.tb07713.x},
  publisher = {Wiley-Blackwell},
  timestamp = {2017-05-31},
}

@Article{Prentice1976a,
  author    = {Ross L. Prentice},
  title     = {A Generalization of the Probit and Logit Methods for Dose Response Curves},
  journal   = {Biometrics},
  year      = {1976},
  volume    = {32},
  number    = {4},
  pages     = {761-768},
  url       = {http://www.jstor.org/stable/2529262},
  abstract  = {The relationship between response probability and dosage in quantal response bioassay is modelled using a four parameter class. In addition to location and scale quantities the model includes two shape parameters that essentially index skewness and heaviness of tails of the dose-response curve. The class of models includes such special cases as the logistic, normal, extreme minimum value, extreme maximum value, double exponential, exponential and reflected exponential distribution functions. Score tests are derived for logistic and normal hypotheses and certain submodels are discussed for which the model fitting is computationally convenient. The data of Bliss [1935] illustrates the potential improvement over usual methods in the estimation of critical dose levels.},
  publisher = {[Wiley, International Biometric Society]},
  timestamp = {2017-05-31},
}

@Book{CarlinLouis2000a,
  author    = {Carlin, Bradley P. and Louis, Thomas A.},
  title     = {Bayes and Empirical Bayes Methods for Data Analysis},
  year      = {2000},
  edition   = {2},
  publisher = {Chapman and Hall/CRC},
  isbn      = {9781584881704},
  timestamp = {2017-05-30},
}

@Unpublished{SpiegelhalterBestGilks1996a,
  author    = {Spiegelhalter, D. J. and Best, A. Thomas N. and Gilks, W. R.},
  title     = {BUGS: Bayesian Inference Using Gibbs Sampling, Version 0.5},
  date      = {1996},
  timestamp = {2017-05-30},
}

@Article{Stukel1988a,
  author    = {Therese A. Stukel},
  title     = {Generalized Logistic Models},
  journal   = {Journal of the American Statistical Association},
  year      = {1988},
  volume    = {83},
  number    = {402},
  pages     = {426-431},
  url       = {http://www.jstor.org/stable/2288858},
  abstract  = {A class of models indexed by two shape parameters is introduced, both to extend the scope of the standard logistic model to asymmetric probability curves and improve the fit in the noncentral probability regions. One-parameter subclasses can be used to examine symmetric or asymmetric deviations from the logistic model. The delta algorithm is adapted to obtain maximum likelihood estimates of the parameters. A review is made of other proposed generalizations. The standard linear logistic model is widely used for modeling the dependence of binary data on explanatory variables. Its success is due to its broad applicability, simplicity of form, and ease of interpretation. This model works well for many common applications; however, it assumes that the expected probability curve μ(η) is skew-symmetric about μ = 1/2 and that the shape of μ(η) is the cumulative distribution function of the logistic distribution. Symmetric data with a shallower or steeper slope of ascent may not be fitted well by this model, nor is there any provision for treating the two tails of the estimated curve μ(η) asymmetrically or fitting different distributions for μ(η). This article introduces a class of models, indexed by one or two shape parameters, that encompasses a wider range of situations than the standard logistic model (although the standard model is included). The shape parameters have been specifically designed to modify the behavior of the curve in the extreme-probability regions where problems of lack of fit may occur, while allowing for asymmetric treatment of the two tails. Members of this family approximate the Gaussian, Laplace, and extreme minimum and maximum distributions up to the first four moments. The model can be collapsed to several simpler one-parameter symmetric and asymmetric formulations.},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  timestamp = {2017-05-31},
}

@Book{Stan2016a,
  author    = {{Stan Development Team}},
  title     = {{Stan} Modeling Language Users Guide and Reference Manual, Version 2.14.0},
  date      = {2016},
  url       = {https://github.com/stan-dev/stan/releases/download/v2.14.0/stan-reference-2.14.0.pdf},
  urldate   = {2017-04-20},
  timestamp = {2017-05-30},
}

@Article{JuarezSteel2010a,
  author       = {Juárez, Miguel A. and Steel, Mark F. J.},
  title        = {Model-based clustering of non-{Gaussian} panel data based on skew-{t} distributions},
  journaltitle = {Journal of Business {\&} Economic Statistics},
  date         = {2010-01},
  volume       = {28},
  number       = {1},
  pages        = {52--66},
  doi          = {10.1198/jbes.2009.07145},
  publisher    = {Informa {UK} Limited},
  timestamp    = {2017-05-30},
}

@Article{Nagler1994a,
  author       = {Nagler, Jonathan},
  title        = {Scobit: An Alternative Estimator to Logit and Probit},
  journaltitle = {American Journal of Political Science},
  date         = {1994},
  volume       = {38},
  number       = {1},
  pages        = {230--255},
  url          = {http://www.jstor.org/stable/2111343},
  publisher    = {[Midwest Political Science Association, Wiley]},
  timestamp    = {2017-05-30},
}

@Article{TomzTuckerWittenberg2002a,
  author    = {Michael Tomz and Joshua A. Tucker and Jason Wittenberg},
  title     = {An Easy and Accurate Regression Model for Multiparty Electoral Data},
  journal   = {Political Analysis},
  year      = {2002},
  volume    = {10},
  number    = {1},
  pages     = {66-83},
  issn      = {10471987, 14764989},
  url       = {http://www.jstor.org/stable/25791665},
  abstract  = {Katz and King have previously proposed a statistical model for multiparty election data. They argue that ordinary least-squares (OLS) regression is inappropriate when the dependent variable measures the share of the vote going to each party, and they recommend a superior technique. Regrettably, the Katz—King model requires a high level of statistical expertise and is computationally demanding for more than three political parties. We offer a sophisticated yet convenient alternative that involves seemingly unrelated regression (SUR). SUR is nearly as easy to use as OLS yet performs as well as the Katz—King model in predicting the distribution of votes and the composition of parliament. Moreover, it scales easily to an arbitrarily large number of parties. The model has been incorporated into Clarify, a statistical suite that is available free on the Internet.},
  publisher = {[Oxford University Press, Society for Political Methodology]},
}

@Article{KatzKing1999a,
  author    = {Jonathan N. Katz and Gary King},
  title     = {A Statistical Model for Multiparty Electoral Data},
  journal   = {American Political Science Review},
  year      = {1999},
  volume    = {93},
  number    = {01},
  month     = {mar},
  pages     = {15--32},
  doi       = {10.2307/2585758},
  publisher = {Cambridge University Press ({CUP})},
}

@Article{Aitchison1982a,
  author    = {J. Aitchison},
  title     = {The Statistical Analysis of Compositional Data},
  journal   = {Journal of the Royal Statistical Society. Series B (Methodological)},
  year      = {1982},
  volume    = {44},
  number    = {2},
  pages     = {139-177},
  issn      = {00359246},
  url       = {http://www.jstor.org/stable/2345821},
  abstract  = {The simplex plays an important role as sample space in many practical situations where compositional data, in the form of proportions of some whole, require interpretation. It is argued that the statistical analysis of such data has proved difficult because of a lack both of concepts of independence and of rich enough parametric classes of distributions in the simplex. A variety of independence hypotheses are introduced and interrelated, and new classes of transformed-normal distributions in the simplex are provided as models within which the independence hypotheses can be tested through standard theory of parametric hypothesis testing. The new concepts and statistical methodology are illustrated by a number of applications.},
  publisher = {[Royal Statistical Society, Wiley]},
}

@Book{Tanner1996a,
  author    = {Tanner, Martin A.},
  title     = {Tools for Statistical Inference},
  year      = {1996},
  publisher = {Springer},
  isbn      = {1461284716},
  pagetotal = {220},
  ean       = {9781461284710},
}

@Article{TomzHouweling2003a,
  author    = {Michael Tomz and Robert P. Van Houweling},
  title     = {How Does Voting Equipment Affect the Racial Gap in Voided Ballots?},
  journal   = {American Journal of Political Science},
  year      = {2003},
  volume    = {47},
  number    = {1},
  month     = {jan},
  pages     = {46--60},
  doi       = {10.1111/1540-5907.00004},
  publisher = {Wiley-Blackwell},
}

@Article{GelmanGoegebeurTuerlinckxEtAl2000a,
  author    = {A. Gelman and Y. Goegebeur and F. Tuerlinckx and I. Van Mechelen},
  title     = {Diagnostic checks for discrete data regression models using posterior predictive simulations},
  journal   = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
  year      = {2000},
  volume    = {49},
  number    = {2},
  pages     = {247--268},
  doi       = {10.1111/1467-9876.00190},
  publisher = {Wiley-Blackwell},
}

@Article{AlbertChib1995a,
  author    = {Albert, Jim and Chib, Siddhartha},
  title     = {Bayesian residual analysis for binary response regression models},
  journal   = {Biometrika},
  year      = {1995},
  volume    = {82},
  number    = {4},
  pages     = {747--769},
  doi       = {10.1093/biomet/82.4.747},
  publisher = {Oxford University Press ({OUP})},
  timestamp = {2017-05-31},
}

@Article{Herron1999a,
  author    = {Michael C. Herron},
  title     = {Postestimation Uncertainty in Limited Dependent Variable Models},
  journal   = {Political Analysis},
  year      = {1999},
  volume    = {8},
  number    = {01},
  pages     = {83--98},
  doi       = {10.1093/oxfordjournals.pan.a029806},
  publisher = {Cambridge University Press ({CUP})},
}

@Article{Krehbiel1995a,
  author    = {Keith Krehbiel},
  title     = {Cosponsors and Wafflers from A to Z},
  journal   = {American Journal of Political Science},
  year      = {1995},
  volume    = {39},
  number    = {4},
  month     = {nov},
  pages     = {906},
  doi       = {10.2307/2111662},
  publisher = {{JSTOR}},
}

@Article{Albert1992a,
  author    = {J. H. Albert},
  title     = {Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling},
  journal   = {Journal of Educational and Behavioral Statistics},
  year      = {1992},
  volume    = {17},
  number    = {3},
  month     = {jan},
  pages     = {251--269},
  doi       = {10.3102/10769986017003251},
  publisher = {American Educational Research Association ({AERA})},
}

@Article{AlbertChib1993a,
  author    = {James H. Albert and Siddhartha Chib},
  title     = {Bayesian Analysis of Binary and Polychotomous Response Data},
  journal   = {Journal of the American Statistical Association},
  year      = {1993},
  volume    = {88},
  number    = {422},
  month     = {jun},
  pages     = {669--679},
  doi       = {10.1080/01621459.1993.10476321},
  publisher = {Informa {UK} Limited},
}

@Article{ClintonJackmanRivers2004a,
  author       = {Clinton, Joshua and Jackman, Simon and Rivers, Douglas},
  title        = {The statistical analysis of roll call data},
  journaltitle = {American Political Science Review},
  date         = {2004-05},
  volume       = {98},
  number       = {02},
  pages        = {355--370},
  doi          = {10.1017/s0003055404001194},
  publisher    = {Cambridge University Press (CUP)},
  timestamp    = {2017-05-31},
}

@Book{EnelowHinich1984a,
  author    = {Enelow, J.M. and Hinich, M.J.},
  title     = {The Spatial Theory of Voting: An Introduction},
  year      = {1984},
  publisher = {Cambridge University Press},
  isbn      = {9780521275156},
  url       = {https://books.google.com/books?id=lXY6AAAAIAAJ},
  lccn      = {lc83007758},
  timestamp = {2017-05-31},
}

@Article{Jackman2000a,
  author    = {Simon Jackman},
  title     = {Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via {Bayesian} Simulation},
  journal   = {Political Analysis},
  year      = {2000},
  volume    = {8},
  number    = {4},
  pages     = {307-332},
  issn      = {10471987, 14764989},
  url       = {http://www.jstor.org/stable/25791616},
  abstract  = {Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including "auxiliary" quantities such as goodness-of-fit statistics, predicted values, and residuals. Bayesian simulation treats these quantities as if they were missing data, sampling from their implied posterior densities. Exploiting this principle also lets researchers estimate models via Bayesian simulation where maximum-likelihood estimation would be intractable. Bayesian simulation thus provides a unified solution for quantitative social science. I elaborate these ideas in a variety of contexts: these include generalized linear models for binary responses using data on bill cosponsorship recently reanalyzed in Political Analysis, item—response models for the measurement of respondent's levels of political information in public opinion surveys, the estimation and analysis of legislators' ideal points from roll-call data, and outlier-resistant regression estimates of incumbency advantage in U.S. Congressional elections.},
  publisher = {[Oxford University Press, Society for Political Methodology]},
  timestamp = {2017-05-31},
}

@Article{Jackman2001a,
  author    = {Simon Jackman},
  title     = {Multidimensional Analysis of Roll Call Data via {Bayesian} Simulation: Identification, Estimation, Inference, and Model Checking},
  journal   = {Political Analysis},
  year      = {2001},
  volume    = {9},
  number    = {3},
  pages     = {227-241},
  issn      = {10471987, 14764989},
  url       = {http://www.jstor.org/stable/25791646},
  abstract  = {Vote-specific parameters are often by-products of roll call analysis, the primary goal being the measurement of legislators' ideal points. But these vote-specific parameters are more important in higher-dimensional settings: prior restrictions on vote parameters help identify the model, and researchers often have prior beliefs about the nature of the dimensions underlying the proposal space. Bayesian methods provide a straightforward and rigorous way for incorporating these prior beliefs into roll call analysis. I demonstrate this by exploiting the close connections among roll call analysis, item—response models, and "full-information" factor analysis. Vote-specific discrimination parameters are equivalent to factor loadings, and as in factor analysis, they (1) enable researchers to discern the substantive content of the recovered dimensions, (2) can be used for assessing dimensionality and model checking, and (3) are an obvious vehicle for introducing and testing researchers' prior beliefs about the dimensions. Bayesian simulation facilitates these uses of discrimination parameters, by simplifying estimation and inference for the massive number of parameters generated by roll call analysis.},
  publisher = {[Oxford University Press, Society for Political Methodology]},
  timestamp = {2017-05-31},
}

@Book{Londregan2007a,
  author    = {Londregan, J.B.},
  title     = {Legislative Institutions and Ideology in {Chile}},
  year      = {2007},
  series    = {Political Economy of Instituti},
  publisher = {Cambridge University Press},
  isbn      = {9780521037266},
  url       = {https://books.google.com/books?id=WpXHHYf7lJoC},
  timestamp = {2017-05-31},
}

@Book{PooleRosenthal2000a,
  author    = {Keith T. Poole and Howard Rosenthal},
  title     = {Congress: A Political-Economic History of Roll Call Voting},
  year      = {2000},
  publisher = {Oxford University Press},
  isbn      = {978-0195142426},
  timestamp = {2017-05-31},
}

@Book{Draper1992a,
  author    = {IDavid Draper},
  title     = {Combining Information: Statistical Issues and Opportunities for Research},
  year      = {1992},
  series    = {Contemporary Statistics},
  publisher = {National Academy Press},
  isbn      = {9780309047302},
  url       = {https://books.google.com/books?id=l0ArAAAAYAAJ},
  lccn      = {92060678},
  timestamp = {2017-05-31},
}

@Article{Western1998a,
  author       = {Western, Bruce},
  title        = {Causal Heterogeneity in Comparative Research: A {Bayesian} Hierarchical Modelling Approach},
  journaltitle = {American Journal of Political Science},
  date         = {1998},
  volume       = {42},
  number       = {4},
  pages        = {1233--1259},
  doi          = {10.2307/2991856},
  url          = {http://www.jstor.org/stable/2991856},
  publisher    = {[Midwest Political Science Association, Wiley]},
  timestamp    = {2017-05-31},
}

@Article{AlvarezGarrettLange1991a,
  author    = {R. Michael Alvarez and Geoffrey Garrett and Peter Lange},
  title     = {Government Partisanship, Labor Organization, and Macroeconomic Performance},
  journal   = {The American Political Science Review},
  year      = {1991},
  volume    = {85},
  number    = {2},
  pages     = {539-556},
  issn      = {00030554, 15375943},
  url       = {http://www.jstor.org/stable/1963174},
  abstract  = {Governments of the Left and Right have distinct partisan economic policies and objectives that they would prefer to pursue. Their propensity to do so, however, is constrained by their desire for reelection. We argue that the ability of governments to further their partisan interests and preside over reelectable macroeconomic outcomes simultaneously is dependent on the organization of the domestic economy, particularly the labor movement. We hypothesize that there are two different paths to desirable macroeconomic performance. In countries with densely and centrally organized labor movements, leftist governments can promote economic growth and reduce inflation and unemployment. Conversely, in countries with weak labor movements, rightist governments can pursue their partisan-preferred macroeconomic strategies and achieve similarly beneficial macroeconomic outcomes. Performance will be poorer in other cases. These hypotheses are supported by analysis of pooled annual time series data for 16 advanced industrial democracies between 1967 and 1984.},
  publisher = {[American Political Science Association, Cambridge University Press]},
  timestamp = {2017-05-31},
}

@Article{DempsterLairdRubin1977a,
  author    = {A. P. Dempster and N. M. Laird and D. B. Rubin},
  title     = {Maximum Likelihood from Incomplete Data via the EM Algorithm},
  journal   = {Journal of the Royal Statistical Society. Series B (Methodological)},
  year      = {1977},
  volume    = {39},
  number    = {1},
  pages     = {1-38},
  issn      = {00359246},
  url       = {http://www.jstor.org/stable/2984875},
  abstract  = {A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.},
  publisher = {[Royal Statistical Society, Wiley]},
}

@Article{Murray1977a,
  author    = {Gordon Murray},
  title     = {Discussion on the Paper by Professor {Dempster} et al.},
  journal   = {Journal of the Royal Statistical Society. Series B (Methodological)},
  year      = {1977},
  volume    = {39},
  number    = {1},
  pages     = {27},
  issn      = {00359246},
  url       = {http://www.jstor.org/stable/2984875},
  publisher = {[Royal Statistical Society, Wiley]},
  timestamp = {2017-05-31},
}

@Book{Congdon2007a,
  author    = {Peter Congdon},
  title     = {{Bayesian} Statistical Modelling},
  year      = {2007},
  publisher = {Wiley},
  isbn      = {978-0-470-01875-0},
}

@Article{Jackman2005a,
  author    = {Simon Jackman},
  title     = {Pooling the polls over an election campaign},
  journal   = {Australian Journal of Political Science},
  year      = {2005},
  volume    = {40},
  number    = {4},
  month     = {dec},
  pages     = {499--517},
  doi       = {10.1080/10361140500302472},
  publisher = {Informa {UK} Limited},
  timestamp = {2017-05-31},
}

@Book{GrunbergMayerSniderman2002a,
  author    = {Gérard Grunberg and Nonna Mayer and Paul M. Sniderman},
  title     = {La Démocratie à l'épreuve : Une nouvelle approche de l'opinion des Français},
  year      = {2002},
  publisher = {Fnsp - Presse de la},
  isbn      = {978-2724608755},
}

@Article{BetancourtGirolami2013a,
  author      = {M. J. Betancourt and Mark Girolami},
  title       = {{Hamiltonian Monte Carlo} for Hierarchical Models},
  date        = {2013-12-03},
  eprint      = {1312.0906v1},
  eprintclass = {stat.ME},
  eprinttype  = {arXiv},
  abstract    = {Hierarchical modeling provides a framework for modeling the complex interactions typical of problems in applied statistics. By capturing these relationships, however, hierarchical models also introduce distinctive pathologies that quickly limit the efficiency of most common methods of in- ference. In this paper we explore the use of Hamiltonian Monte Carlo for hierarchical models and demonstrate how the algorithm can overcome those pathologies in practical applications.},
  file        = {online:http\://arxiv.org/pdf/1312.0906v1:PDF},
  keywords    = {stat.ME},
}

@Data{Herron2010a,
  author    = {Michael C. Herron},
  title     = {Replication data for: Post-Estimation Uncertainty in Limited Dependent Variable Models},
  year      = {2010},
  doi       = {1902.1/11199},
  url       = {http://hdl.handle.net/1902.1/11199},
  publisher = {Harvard Dataverse},
}

@InCollection{Liu2005a,
  author    = {Liu, Chuanhai},
  title     = {Robit regression: a simple robust alternative to logistic and probit regression},
  booktitle = {Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives},
  date      = {2005-07},
  publisher = {John Wiley {\&} Sons, Ltd},
  pages     = {227--238},
  doi       = {10.1002/0470090456.ch21},
  timestamp = {2017-06-06},
}

@Article{HanmerKalkan2012a,
  author       = {Hanmer, Michael J. and Kalkan, Kerem Ozan},
  title        = {Behind the curve: clarifying the best approach to calculating predicted probabilities and marginal effects from limited dependent variable models},
  journaltitle = {American Journal of Political Science},
  date         = {2012-07},
  volume       = {57},
  number       = {1},
  pages        = {263--277},
  doi          = {10.1111/j.1540-5907.2012.00602.x},
  publisher    = {Wiley-Blackwell},
  timestamp    = {2017-06-06},
}

@Article{KingTomzWittenberg2000a,
  author       = {Gary King and Michael Tomz and Jason Wittenberg},
  title        = {Making the Most of Statistical Analyses: Improving Interpretation and Presentation},
  journaltitle = {American Journal of Political Science},
  date         = {2000},
  volume       = {44},
  number       = {2},
  pages        = {347--361},
  url          = {http://www.jstor.org/stable/2669316},
  publisher    = {[Midwest Political Science Association, Wiley]},
  timestamp    = {2017-06-07},
}

@Article{Linzer2013a,
  author    = {Drew A. Linzer},
  title     = {Dynamic Bayesian Forecasting of Presidential Elections in the States},
  journal   = {Journal of the American Statistical Association},
  year      = {2013},
  volume    = {108},
  number    = {501},
  pages     = {124-134},
  doi       = {10.1080/01621459.2012.737735},
  eprint    = {http://dx.doi.org/10.1080/01621459.2012.737735},
  url       = { 
        http://dx.doi.org/10.1080/01621459.2012.737735
    
},
  timestamp = {2017-06-07},
}

@Book{Jackman2009a,
  author    = {Jackman, Simon},
  title     = {{Bayesian} Analysis for the Social Sciences},
  year      = {2009},
  date      = {2009-10-23},
  publisher = {John Wiley and Sons Ltd},
  isbn      = {0470011548},
  pagetotal = {598},
  ean       = {9780470011546},
  timestamp = {2017-06-07},
}

@Comment{jabref-meta: databaseType:biblatex;}