منابع مشابه
Ratio Limit Theorems for Markov Chains
Introduction. We consider Markov chains with stationary transition probabilities and state space S = 0, 1, 2, •• -, in both discrete and continuous time. As in [l ] the transition probabilities are denoted by pi}1 (indiscrete time) and pa(t) (in continuous time). We assume that the chains considered are irreducible and recurrent, and in addition that discrete time chains are aperiodic and that ...
متن کاملCentral Limit Theorems for Conditional Markov Chains
This paper studies Central Limit Theorems for real-valued functionals of Conditional Markov Chains. Using a classical result by Dobrushin (1956) for non-stationary Markov chains, a conditional Central Limit Theorem for fixed sequences of observations is established. The asymptotic variance can be estimated by resampling the latent states conditional on the observations. If the conditional means...
متن کاملLimit Theorems for subgeometric Markov chains
This paper studies limit theorems for Markov Chains with general state space under conditions which imply subgeometric ergodicity. We obtain a central limit theorem and moderate deviation principles for additive not necessarily bounded functional of the Markov chains under drift and minorization conditions which are weaker than the Foster-Lyapunov conditions. The regeneration-split chain method...
متن کاملAncestral lineages and limit theorems for branching Markov chains
We consider a branching model in discrete time where each individual has a trait in some general state space. Both the reproduction law and the trait inherited by the offsprings may depend on the trait of the mother and the environment. We study the long time behavior of the population and the ancestral lineage of typical individuals under general assumptions, which we specify for applications ...
متن کاملLimit Theorems for Quadratic Forms of Markov Chains
We develop a martingale approximation approach to studying the limiting behavior of quadratic forms of Markov chains. We use the technique to examine the asymptotic behavior of lag-window estimators in time series and we apply the results to Markov Chain Monte Carlo simulation. As another illustration, we use the method to derive a central limit theorem for U-statistics with varying kernels.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Mathematical Analysis and Applications
سال: 2016
ISSN: 0022-247X
DOI: 10.1016/j.jmaa.2016.05.022