Statistical analysis with missing data
Statistical analysis with missing data
Computers and Operations Research
Comparison of semiparametric and parametric methods for estimating copulas
Computational Statistics & Data Analysis
On sovereign credit migration: A study of alternative estimators and rating dynamics
Computational Statistics & Data Analysis
Short communication: AMCMC: An R interface for adaptive MCMC
Computational Statistics & Data Analysis
Copula model evaluation based on parametric bootstrap
Computational Statistics & Data Analysis
Bayesian Core: A Practical Approach to Computational Bayesian Statistics
Bayesian Core: A Practical Approach to Computational Bayesian Statistics
Statistics and Computing
Bayesian Solutions to the Label Switching Problem
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Short communication: Grapham: Graphical models with adaptive random walk Metropolis algorithms
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Estimation from aggregate data
Computational Statistics & Data Analysis
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
An Introduction to Copulas
Markov chain models for delinquency: Transition matrix estimation and forecasting
Applied Stochastic Models in Business and Industry
Bayesian Time Series Models
| Hi-index | 0.03 |
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.