The mixture transition distribution model (MTD) was introduced in 1985
by Raftery for the modeling of high-order Markov chains with a finite state
space. Since then it has been generalized and successfully applied to a range of
situations, including the analysis of wind directions, DNA sequences and social behavior.
Here we review the MTD model and the developments since 1985. We first introduce the
basic principle and then we present several extensions, including general state spaces
and spatial statistics. Following that, we review methods for estimating the model
parameters. Finally, a review of different types of applications shows the practical
interest of the MTD model.
Publié le : 2002-08-14
Classification:
Mixture transition distribution (MTD) model,
Markov chains,
high-order dependences,
time series,
GMTD model,
EM algorithm,
spatial statistics,
DNA,
social behavior,
wind,
financial time series
@article{1042727943,
author = {Berchtold, Andr\'e and Raftery, Adrian},
title = {The Mixture Transition Distribution Model for High-Order Markov Chains and Non-Gaussian Time Series},
journal = {Statist. Sci.},
volume = {17},
number = {1},
year = {2002},
pages = { 328-356},
language = {en},
url = {http://dml.mathdoc.fr/item/1042727943}
}
Berchtold, André; Raftery, Adrian. The Mixture Transition Distribution Model for High-Order Markov Chains and Non-Gaussian Time Series. Statist. Sci., Tome 17 (2002) no. 1, pp. 328-356. http://gdmltest.u-ga.fr/item/1042727943/