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Estimating discrete Markov models from various incomplete data schemes

Listed author(s):
  • Pasanisi, Alberto
  • Fu, Shuai
  • Bousquet, Nicolas
Registered author(s):

    The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a case, the estimation of transition probabilities is straightforwardly made by counting one-step moves from a given state to another. In many real-life problems, however, the inference is much more difficult as state sequences are not fully observed, namely the state of each individual is known only for some given values of the time variable. A review of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms to perform Bayesian inference and evaluate posterior distributions of the transition probabilities in this missing-data framework. Leaning on the dependence between the rows of the transition matrix, an adaptive MCMC mechanism accelerating the classical Metropolis–Hastings algorithm is then proposed and empirically studied.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 9 ()
    Pages: 2609-2625

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2609-2625
    DOI: 10.1016/j.csda.2012.02.027
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    1. David J. Nott & Robert Kohn, 2005. "Adaptive sampling for Bayesian variable selection," Biometrika, Biometrika Trust, vol. 92(4), pages 747-763, December.
    2. Matthew T Jones, 2005. "Estimating Markov Transition Matrices Using Proportions Data; An Application to Credit Risk," IMF Working Papers 05/219, International Monetary Fund.
    3. Christian Genest & Jean-François Quessy & Bruno Rémillard, 2006. "Goodness-of-fit Procedures for Copula Models Based on the Probability Integral Transformation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 337-366.
    4. Jerome A. Dupuis & Carl James Schwarz, 2007. "A Bayesian Approach to the Multistate Jolly–Seber Capture–Recapture Model," Biometrics, The International Biometric Society, vol. 63(4), pages 1015-1022, December.
    5. Vihola, Matti, 2010. "Grapham: Graphical models with adaptive random walk Metropolis algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 49-54, January.
    6. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "On sovereign credit migration: A study of alternative estimators and rating dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3448-3469, April.
    7. MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-198, January.
    8. Nikoloulopoulos, Aristidis K. & Karlis, Dimitris, 2008. "Copula model evaluation based on parametric bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3342-3353, March.
    9. Gouno, E. & Courtrai, L. & Fredette, M., 2011. "Estimation from aggregate data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 615-626, January.
    10. Strid, Ingvar & Giordani, Paolo & Kohn, Robert, 2010. "Adaptive hybrid Metropolis-Hastings samplers for DSGE models," SSE/EFI Working Paper Series in Economics and Finance 724, Stockholm School of Economics.
    11. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
    12. repec:dau:papers:123456789/1906 is not listed on IDEAS
    13. Isabelle Deltour & Sylvia Richardson & Jean-Yves Le Hesran, 1999. "Stochastic Algorithms for Markov Models Estimation with Intermittent Missing Data," Biometrics, The International Biometric Society, vol. 55(2), pages 565-573, 06.
    14. Rosenthal, Jeffrey S., 2007. "AMCMC: An R interface for adaptive MCMC," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5467-5470, August.
    15. Huard, David & Evin, Guillaume & Favre, Anne-Catherine, 2006. "Bayesian copula selection," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 809-822, November.
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