Markov Chain Monte Carlo Analysis of Correlated Count Data

By Siddhartha Chib, R. Winkelmann. 2001

132 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3e2e1cf38853d81d3c41012154ec3edf538e937c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gael M. Martin
2. Pravin K. Trivedi
3. David M. Zimmer

In Statistics and Probability

1. Gael M. Martin
2. Brendan McCabe
3. Pravin K. Trivedi
4. David M. Zimmer
5. Christian H. Weiβ
6. Xanthi Pedeli
7. Dimitris Karlis
8. W. Barreto‐Souza

Citations in the corpus

• Bayesian predictions of low count time series (2005, background): “For example, West, Harrison and Migon (1985), West and Harrison (1997), Chib, Greenberg and Winkelmann (1998), Durbin and Koopman (2000) and Chib and Winkelmann (2001) all apply Bayesian inferential procedures to dynamic latent factor models of count time series.”
• Copula Modeling: An Introduction for Practitioners (2007, background): “Similarly for the case of joint discrete distributions, a number of studies that explore models of correlated count variables, without explicitly 5 using copulas, are developed in Cameron et al. (1988), Munkin and Trivedi (1999), and Chib and Winkelmann (2001).”