Background Reading

Bayesian Statistics
[1] Gelman, Andrew, John B. Carlin, Hal S. Stern, and Donald B. Rubin (1995). Bayesian Data Analysis. Chapman and Hall.
[2] Berger, James O. (1980). Statistical Decision Theory and Bayesian Analysis. Springer Verlag.
[3] Jackman, Simon (2000). Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo. American Journal of Political Science 44:375-404.
[4] Albert, James H. and Siddhartha Chib (1993). Bayesian Analysis of Binary and Polychotomous Response Data. Journal of the American Statistical Association 88:669-679.
[5] Chib, Siddhartha (1995). Marginal Likelihood from the Gibbs Output. Journal of the American Statistical Association 90:1313-1321.
[6] Chin, Siddhartha and Ivan Jeliazkov (2001). Marginal Likelihood from the Metropolis-Hastings Output. Journal of the American Statistical Association 96:270-281.
[7]  Clarke, Kevin A. (2000). The Effects of Priors on Approximate Bayes Factors from MCMC Output. Working Paper.
[8] Green, Peter J. (1995). Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination. Biometrika 82:711-732.
[9] Han, Cong and Bradley P. Carlin (2001). MCMC Methods for Computing Bayes Factors: A Comparative Review. Working Paper.
[10] Neal, Radford M. (2003). Slice Sampling. Annals of Statistics 31:705-767.
[11] Gilks, W. R. and P. Wild (1992). Adaptive Rejection Sampling for Gibbs Sampling. Applied Statistics 41:337-348.
[12] Neal, Radform M. (2011). MCMC using Hamiltonian Dynamics. In Handbook of Markov Chain Monte Carlo.

Text Analysis
[13] Grimmer, Justin and Brandon Stewart (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis 21:267-297.
[14] Blei, D. M., A. Y. Ng, and M. I. Jordan (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3:993-1022.
[15] Blei, D. M. and M. I. Jordan (2006). Variational Inference for Dirichlet Process Mixtures. Bayesian Analysis 1:121-143.
[16] Blei, D. M. and John D. Lafferty (2007). A Correlated Topic Model of Science. Annals of Applied Statistics 1:17-35.
[17] Jurafsky, Daniel, and James H. Martin (2008). Speech and Language Processing. Prentice Hall.
[18] Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schtze (2008). Introduction to Information Retrieval. Cambridge University Press.

Machine Learning
[19] Bishop, Christopher (2006). Pattern Recognition and Machine Learning. Springer.
[20] Hastie, Trevor, Robert Tibshirani, and Jerome Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.

Nonparametrics
[21] Silverman, Bernard (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall.
[22] Hardle, W. (1992). Applied Nonparametric Regression. Cambridge University Press.
[23] Pagan, Adrian, and Amman Ullah (1999). Nonparametric Econometrics. Cambridge University Press.
[24] Ichimura, Hidehikpo and Petra Todd (2006). Implementing Nonparametric and Semiparametric Estimators. In Handbook of Econometrics, Volume 6.
[25] Rosenbaum, P. and D. Rubin (1983). The Central Role of the Propensity Score in Observational Studies of Causal Effects. Biometrika 70:41-55.
<[27] Abadie, Alberto and Guido Imbens (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica 74:235-267.
[28] Abadie, Alberto, and Guido Imbens (2008). On the Failure of the Bootstrap for Matching Estimators. Econometrica 76:1537-1557.
[29] Siroky, David S. (2009). Navigating Random Forests and Related Advances in Algorithmic Modeling. Statistical Surveys 3:147-163.

Ideal Point Estimation
[30] Lord, Frederic M. (1980). Application of Item Response Theory to Practical Testing Problems. Lawrence Erlbaum Associates.
[31] Poole, Keith T., and Howard Rosenthal (1997). Congress: A Political-Economic History of Roll Call Voting. Oxford University Press.
[32] Poole, Keith T. (2005). Spatial Models of Parliamentary Voting. Cambridge University Press.
[33] Rivers, Douglas (2003). Identification of Multidimensional Spatial Voting Models. Working Paper.
[34] Heckman, James J. and James M. Snyder (1997). Linear Probability Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators. RAND Journal of Economics 28:S142-189.
[35] Lewis, Jeff, and Keith T. Poole (2004). Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap. Political Analysis 12:105-127.
[36] Londregan, John (2000). Estimating Legislator's Preferred Points. Political Analysis 8:35-56.
[37] Lewis, Jeff (2001). Estimating Voter Preference Distributions from Individual-Level Voting Data. Political Analysis 9:275-297.
[38] Bailey, Michael A. (2001). Ideal Point Estimation with a Small Number of Votes: A Random Effects Approach. Political Analysis 9:192-210.
[39] Poole, Keith T. (1998). Recovering a Basic Space from a Set of Issue Scales. American Journal of Political Science 42-954-993.
[40] Poole, Keith T. (2000). Non-parametric Unfolding of Binary Data. Political Analysis 8:211-237.
[41] Imai, Kosuke, James Lo, and Jonathan OImsted (forthcoming). Fast Estimation of Ideal Points with Massive Data. Forthcoming in American Political Science Review.