Abstract
Bayesian statistics is based up a philosophy different from that of other methods of statistical inference. In Bayesian statistics all unknowns, and in particular unknown parameters, are considered to be random variables and their probability distributions specify our beliefs about their likely values. Estimation, model selection, and uncertainty analysis are implemented by using Bayes’s theorem to update our beliefs as new data are observed.
References
- Albert, J. (2007) Bayesian Computation with R, Springer, New York.CrossRefMATHGoogle Scholar
- Albert, J. H. and Chib, S. (1993) Bayes inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts, Journal of Business & Economic Statistics, 11, 1–15.Google Scholar
- Berger, J. O. (1985) Statistical Decision Theory and Bayesian Analysis 2nd ed., Springer-Verlag, Berlin.CrossRefMATHGoogle Scholar
- Bernardo, J. M., and Smith, A. F. M. (1994) Bayesian Theory, Wiley, Chichester.CrossRefMATHGoogle Scholar
- Box, G. E. P., and Tiao, G. C. (1973) Bayesian Inference in Statistical Analysis, Addison-Wesley, Reading, MA.MATHGoogle Scholar
- Brooks, S. P. and Gelman, A. (1998) General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics, 7, 434–455.MathSciNetGoogle Scholar
- Carlin, B. P., and Louis, T. A. (2000) Empirical Bayes: Past, present and future. Journal of the American Statistical Association, 95, 1286–1289.CrossRefMATHMathSciNetGoogle Scholar
- Carlin, B., and Louis, T. A. (2008) Bayesian Methods for Data Analysis, 3rd ed., Chapman & Hall, New York.MATHGoogle Scholar
- Chib, S., and Ergashev, B. (2009) Analysis of multifactor affine yield curve models. Journal of the American Statistical Association, 104, 1324–1337.CrossRefMATHMathSciNetGoogle Scholar
- Chib, S., and Greenberg, E. (1994) Bayes inference in regression models with ARMA(p, q) errors. Journal of Econometrics, 64, 183–206.CrossRefMATHMathSciNetGoogle Scholar
- Chib, S., and Greenberg, E. (1995) Understanding the Metropolis–Hastings algorithm. American Statistician, 49, 327–335.Google Scholar
- Congdon, P. (2001) Bayesian Statistical Modelling, Wiley, Chichester.MATHGoogle Scholar
- Congdon, P. (2003) Applied Bayesian Modelling, Wiley, Chichester.CrossRefMATHGoogle Scholar
- Daniels, M. J., and Kass, R. E. (1999) Nonconjugate Bayesian estimation of covariance matrices and its use in hierarchical models. Journal of the American Statistical Association, 94, 1254–1263.CrossRefMATHMathSciNetGoogle Scholar
- Edwards, W. (1982) Conservatism in human information processing. In Judgement Under Uncertainty: Heuristics and Biases, D. Kahneman, P. Slovic, and A. Tversky, ed., Cambridge University Press, New York.Google Scholar
- Gelman, A., and Rubin, D. B. (1992) Inference from iterative simulation using multiple sequence (with discussion). Statistical Science, 7, 457–511.CrossRefGoogle Scholar
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013) Bayesian Data Analysis, 3rd ed., Chapman & Hall, London.Google Scholar
- Greyserman, A., Jones, D. H., and Strawderman, W. E. (2006) Portfolio selection using hierarchical Bayesian analysis and MCMC methods, Journal of Banking and Finance, 30, 669–678.CrossRefGoogle Scholar
- Kass, R. E., Carlin, B. P., Gelman, A., and Neal, R. (1998) Markov chain Monte Carlo in practice: A roundtable discussion. American Statistician, 52, 93–100.MathSciNetGoogle Scholar
- Kim, S., Shephard, N., and Chib, S. (1998) Stochastic volatility: likelihood inference and comparison with ARCH models.Review of Economic Studies, 65, 361–393.Google Scholar
- Ledoit, O., and Wolf, M. (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10, 603–621.CrossRefGoogle Scholar
- Lehmann, E. L. (1983) Theory of Point Estimation, Wiley, New York.CrossRefMATHGoogle Scholar
- Lunn, D., Jackson, C., Best, N., Thomas, A., and Spiegelhalter, D. (2013) The BUGS Book, Chapman & Hall.Google Scholar
- Lunn, D. J., Thomas, A., Best, N., and Spiegelhalter, D. (2000) OpenBUGS—A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing, 10, 325–337.CrossRefGoogle Scholar
- Rachev, S. T., Hsu, J. S. J., Bagasheva, B. S., and Fabozzi, F. J. (2008) Bayesian Methods in Finance, Wiley, Hoboken, NJ.Google Scholar
- Robert, C. P. (2007) The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, 2nd ed., Springer, New York.Google Scholar
- Robert, C. P., and Casella, G. (2005) Monte Carlo Statistical Methods, 2nd ed., Springer, New York.Google Scholar
- Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B, Methodological, 64, 583–616.CrossRefMATHGoogle Scholar
- Stein, C. (1956) Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. In Proceedings of the Third Berkeley Symposium on Mathematical and Statistical Probability, J. Neyman, ed., University of California, Berkeley, pp. 197–206, Volume 1.Google Scholar
- van der Vaart, A. W. (1998) Asymptotic Statistics, Cambridge University Press, Cambridge.CrossRefMATHGoogle Scholar
Copyright information
© Springer Science+Business Media New York 2015