Add to ORKGResults and conditions that quantify the decrease in dependence with lag for stationary Markov chains are obtained. Notions of dependence that are used are the concordance or positive quadrant dependence ordering, measures of dependence based on ^-divergences such as the relative entropy measure of de-pendence, and the Goodman-Kruskal measure of association. The general results are mainly for first-order Markov chains, but there are also some results for higher order Markov chains. 1
International audienceWe prove an invariance principle for non-stationary random processes and estab...
A Cox process NCox directed by a stationary random measure ξ has second moment var NCox(0, t] = E(ξ(...
<p>Information theoretic measures used for the inference of causality and the analysis of causal eff...
Results and conditions that quantify the decrease in dependence with lag for stationary Markov chain...
We study the property of dependence in lag for Markov chains on countable partially ordered state sp...
AbstractA general notion of positive dependence among successive observations in a finite-state stat...
Let Y = (Y (t), t ≥ 0) be a stationary homogeneous Markov process with partially ordered state space...
In the study of random processes, dependence is the rule rather than the exception. To facilitate th...
Two families of dependence measures between random variables are introduced. They are based on the R...
Gives an account of the developments in the field of probability and statistics for dependent data. ...
The most common measure of dependence is the correlation coefficient. Its problem is that it can be ...
We propose two methods to measure all (linear and nonlinear) statistical dependences in a stationary...
We show that a deeper insight into the relations among marginal processes of a multivariate Markov c...
International audienceWe study the positive dependence of pairs of stochastic processes and examine ...
summary:For data generated by stationary Markov chains there are considered estimates of chain param...
International audienceWe prove an invariance principle for non-stationary random processes and estab...
A Cox process NCox directed by a stationary random measure ξ has second moment var NCox(0, t] = E(ξ(...
<p>Information theoretic measures used for the inference of causality and the analysis of causal eff...
Results and conditions that quantify the decrease in dependence with lag for stationary Markov chain...
We study the property of dependence in lag for Markov chains on countable partially ordered state sp...
AbstractA general notion of positive dependence among successive observations in a finite-state stat...
Let Y = (Y (t), t ≥ 0) be a stationary homogeneous Markov process with partially ordered state space...
In the study of random processes, dependence is the rule rather than the exception. To facilitate th...
Two families of dependence measures between random variables are introduced. They are based on the R...
Gives an account of the developments in the field of probability and statistics for dependent data. ...
The most common measure of dependence is the correlation coefficient. Its problem is that it can be ...
We propose two methods to measure all (linear and nonlinear) statistical dependences in a stationary...
We show that a deeper insight into the relations among marginal processes of a multivariate Markov c...
International audienceWe study the positive dependence of pairs of stochastic processes and examine ...
summary:For data generated by stationary Markov chains there are considered estimates of chain param...
International audienceWe prove an invariance principle for non-stationary random processes and estab...
A Cox process NCox directed by a stationary random measure ξ has second moment var NCox(0, t] = E(ξ(...
<p>Information theoretic measures used for the inference of causality and the analysis of causal eff...