185 Dynamic Bayesian Models for Discrete-Valued Time Series
Czado, C. and Song, P. X.-K. (2008), State space mixed models for longitudinal observations with
binary and binomial responses, Statistical Papers, 49, 691–714.
Davis, R. A. and Dunsmuir, W. T. M. (2015). State space models for count time series. In R. A.
Davis, S. H. Holan, R. Lund and N. Ravishanker, eds., Handbook of Discrete-Valued Time Series,
pp. 121–144. Chapman & Hall, Boca Raton, FL.
Davis, R. and Rodriguez-Yam, G. (2005), Estimation for state-space models based on a likelihood
approximation, Statistica Sinica, 15, 381–406.
de Jong, P. and Shephard, N. (1995), The simulation smoother for time series models, Biometrika,
82, 339–350.
Durbin, J. and Koopman, S. J. (2001), Time Series Analysis by State Space Methods, Oxford, U.K.: Oxford
University Press.
Fearnhead, P. (2011), MCMC for state space models, in Handbook of Markov Chain Monte Carlo,eds.
S. Brooks, A. Gelman, G. L. Jones, and X.-L. Meng, New York: CRC, pp. 513–529.
Ferreira, M. A. R. and Gamerman, D. (2000), Dynamic generalized linear models, in Generalized Linear
Models: A Bayesian Perspective, eds. D. K. Dey, S. K. Ghosh, and B. Mallick, New York: CRC,
pp. 57–71.
Fonseca, T. C. O., Ferreira, M. A. R., and Migon, H. S. (2008), Objective Bayesian analysis for the
Student-t regression model, Biometrika, 95, 325–333.
Gamerman, D. (1997), Efcient sampling from the posterior distribution in generalized linear mixed
models, Statistics and Computing, 7, 57–68.
Gamerman, D. (1998), Markov chain Monte Carlo for dynamic generalized linear models, Biometrika,
85, 215–227.
Gamerman, D., Santos, T. R., and Franco, G. C. (2013), A non-Gaussian family of state-space models
with exact marginal likelihood, Journal of Time Series Analysis, 35, 625–645.
Geweke, J. and Tanizaki, H. (2001), Bayesian estimation of state-space models using the Metropolis-
Hastings algorithm within Gibbs sampling, Computational Statistics & Data Analysis, 37,
151–170.
Giordani, P. and Kohn, R. (2010), Adaptive independent Metropolis-Hastings by fast estimation of
mixtures of normals, Journal of Computational and Graphical Statistics, 19, 243–259.
Gordon, N. J., Salmond, D. J., and Smith, A. F. M. (1993), A novel approach to non-linear and
non-Gaussian Bayesian state estimation, IEE Proceedings of Radar and Signal Processing, 140,
107–113.
Harvey, A. C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge, U.K.:
Cambridge University Press.
Lee, Y. and Nelder, J. A. (1999), Hierarchical generalized linear models (with discussion), Journal of
the Royal Statistical Society, Series B, 58, 619–678.
Le Strat, Y. and Carrat, F. (1999), Monitoring epidemiologic surveillance data using hidden Markov
models, Statistics in Medicine, 18, 3463–3478.
Mallick, B. and Gelfand, A. (1994), Generalized linear models with unknown link function, Biometrika,
81, 237–245.
Martins, T. G., Simpson, D., Lindgren, F., and Rue, H. (2013), Bayesian computing with INLA: New
features, Computational Statistics & Data Analysis, 67(C), 68–83.
Meinhold, R. J. and Singpurwalla, N. D. (1989), Robustication of Kalman lter, Journal of the American
Statistical Association, 84, 479–448.
Migon, H. S., Gamerman, D., Lopes, H. F., and Ferreira, M. A. R. (2005), Dynamic models, in Hand-
Book of Statistics - Bayesian Statistics: Modeling and Computation,eds.C.R.RaoandD.K.Dey,
Amsterdam, the Netherlands: Elsevier, pp. 553–588.
Nelder, J. A. and Wedderburn, R. W. M. (1972), Generalized linear models, Journal of the Royal
Statistical Society, Series A, 135, 370–384.
Pitt, M. K. and Shephard, N. (1999), Filtering via simulation: Auxiliary particle lters, Journal of the
American Statistical Association, 94, 590–599.
Pitt, M. K., Silva, R. S., Giordani, P., and Kohn, R. (2012), On some properties of Markov chain Monte
Carlo simulation methods based on the particle lter, Journal of Econometrics, 171(2), 134–151.
186 Handbook of Discrete-Valued Time Series
Polson, N. G., Scott, J. G., and Windle, J. (2013), “Bayesian inference for logistic models using
PólyaGamma latent variables, Journal of the American Statistical Association, 108, 1339–1349.
R Core Team (2013), R: A Language and Environment for Statistical Computing, R Foundation for
Statistical Computing, Vienna, Austria. http://www.R-project.org/.
Roberts, G. O., Gelman, A., and Gilks, W. R. (1997), Weak convergence and optimal scaling of random
walk Metropolis algorithms, Annals of Applied Probability, 7(1), 110–120.
Roberts, G. O. and Rosenthal, J. S. (2009), Examples of adaptive MCMC, Journal of Computational and
Graphical Statistics, 18, 349–367.
Rue, H. and Held, L. (2005), Gaussian Markov Random Fields: Theory and Applications, Vol. 104 of
Monographs on Statistics and Applied Probability, London, U.K.: Chapman & Hall.
Rue, H., and Martino, S. (2007), Approximate Bayesian inference for hierarchical Gaussian Markov
random eld models, Journal of Statistical Planning and Inference, 137(10), 3177–3192.
Rue, H., Martino, S., and Chopin, N. (2009), Approximate Bayesian inference for latent Gaussian
models by using integrated nested Laplace approximations, Journal of the Royal Statistical Society:
Series B(Statistical Methodology), 71(2), 319–392.
Ruiz-Cárdenas, R., Krainski, E., and Rue, H. (2011), Direct tting of dynamic models using integrated
nested Laplace approximations–INLA, Computational Statistics & Data Analysis.
Shephard, N. and Pitt, M. K. (1997), Likelihood analysis of non-Gaussian measurement time series,
Biometrika, 84, 653–667.
Stoffer, D. S., Schert, M. S., Richardson, G. A., Day, N. L., and Coble, P. A. (1998), A Walsh-Fourier
analysis of the effects of moderate maternal alcohol consumption on neonatal sleep-state
cycling, Journal of the American Statistical Association, 83, 954–963.
Tierney, L. (1994), Markov chains for exploring posterior distributions, Annals of Statistics, 22(4),
1701–1728.
Tierney, L. and Kadane, J. (1986), Accurate approximations for posterior moments and marginal
densities, Journal of the American Statistical Association, 81(393), 82–86.
Valdebenito, A., Arellano-Valle, R., Romeo, J.S., Torres-Avilés, F. (2015), A skew-normal dynamic
linear model and bayesian forecasting. Universidad de Chile. (Submitted).
Watanabe, T. (2004), A multi-move sampler for estimating non-Gaussian time series models: Com-
ments on Shephard & Pitt (1997), Biometrika, 91, 246–248.
West, M. and Harrison, J. (1997), Bayesian Forecasting and Dynamic Models, 2nd ed., New York:
Springer-Verlag.
West, M., Harrison, P. J., and Migon, H. S. (1985), Dynamic generalized linear models and Bayesian
forecasting (with discussion), JournaloftheAmericanStatisticalAssociation, 80, 73–83.
Zeger, S. (1988), A regression model for time series of counts, Biometrika, 75, 621–629.
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