Estimating discrete Markov models from various incomplete data schemes
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 56 (2012)
Issue (Month): 9 ()
|Contact details of provider:|| Web page: http://www.elsevier.com/locate/csda|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- David J. Nott & Robert Kohn, 2005. "Adaptive sampling for Bayesian variable selection," Biometrika, Biometrika Trust, vol. 92(4), pages 747-763, December.
- Gouno, E. & Courtrai, L. & Fredette, M., 2011. "Estimation from aggregate data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 615-626, January.
- 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.
- Huard, David & Evin, Guillaume & Favre, Anne-Catherine, 2006. "Bayesian copula selection," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 809-822, November.
- 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.
- Vihola, Matti, 2010. "Grapham: Graphical models with adaptive random walk Metropolis algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 49-54, January.
- Matthew T Jones, 2005. "Estimating Markov Transition Matrices Using Proportions Data; An Application to Credit Risk," IMF Working Papers 05/219, .
- Rosenthal, Jeffrey S., 2007. "AMCMC: An R interface for adaptive MCMC," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5467-5470, August.
- 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.
- Elena Kalotychou & Ana-Maria Fuertes, 2006. "On Sovereign Credit Migration: A Study of Alternative Estimators and Rating Dynamics," Computing in Economics and Finance 2006 509, Society for Computational Economics.
- repec:dau:papers:123456789/1906 is not listed on IDEAS
- 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.
- MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-198, January.
- 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.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2609-2625. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.