arXiv subjects
Publications and source records attributed to Yinna Ye.
In this paper, we study the asymptotic behaviour for distribution of supercritical branching process in a Markovian environment with linear fractional offspring distributions. A phase transition in the behaviour of the process is discovered, through investigating the strongly and intermediately strongly supercritical cases.
This article shows the geometric decay rate of Euler-Maruyama scheme for one-dimensional stochastic differential equation towards its invariant probability measure under total variation distance. Firstly, the existence and uniqueness of invariant probability measure and the uniform geometric ergodicity of the chain are studied through introduction of non-atomic Markov chains. Secondly, the equivalent conditions for uniform geometric ergodicity of the chain are discovered, by constructing a split Markov chain based on the original Euler-Maruyama scheme.
Consider $(Z_n)_{n\geq0}$ a supercritical branching process in an independent and identically distributed environment. Based on some recent development in martingale limit theory, we established law of the iterated logarithm, strong law of large numbers, invariance principle and optimal convergence rate in the central limit theorem under Zolotarev and Wasserstein distances of order $p\in(0,2]$ for the process $(\log Z_n)_{n\geq0}$.
Let $ (Z_{n})_{n\geq 0} $ be a supercritical branching process in an independent and identically distributed random environment. We establish an optimal convergence rate in the Wasserstein-$1$ distance for the process $ (Z_{n})_{n\geq 0} $, which completes a result of Grama et al. [Stochastic Process. Appl., 127(4), 1255-1281, 2017]. Moreover, an exponential nonuniform Berry-Esseen bound is also given. At last, some applications of the main results to the confidence interval estimation for the criticality parameter and the population size $Z_n$ are discussed.
Let $\{Z_{1,n} , n\geq 0\}$ and $\{Z_{2,n}, n\geq 0\}$ be two supercritical branching processes in different random environments, with criticality parameters $μ_1$ and $μ_2$ respectively. It is known that $\frac{1}{n} \ln Z_{1,n} \rightarrow μ_1$ and $\frac{1}{m} \ln Z_{2,m} \rightarrow μ_2$ in probability as $m, n \rightarrow \infty.$ In this paper, we are interested in the comparison on the two criticality parameters. To this end, we prove a non-uniform Berry-Esseen's bound and Cramér's moderate deviations for $\frac{1}{n} \ln Z_{1,n} - \frac{1}{m} \ln Z_{2,m}$ as $m, n \rightarrow \infty.$ An application is also given for constructing confidence interval for $μ_1-μ_2$.
Suppose that $(Z_n)_{n\geq0}$ is a supercritical branching process in independent and identically distributed random environment. The right tail function of the scaled growth rate for $(Z_n)_{n\geq0}$ is studied. The upper bounds for $\displaystyle\mathbb{P}\left[\frac{\log Z_n}{Mn}-μ\geq x\right]$ for any $x\geq3$ are obtained, by applying an extension of the Hoeffding type inequalities.
Let $(Ω,\mathcal{F}, \mathbb{P})$ be a probability space and $E$ be a finite set. Assume that $X=(X_n)$ is an irreducible and aperiodic Markov chain, defined on $(Ω,\mathcal{F}, \mathbb{P})$, with values in $E$ and with transition probability $P=\Big(p_{i,j}\Big)_{i,j}$. Let $(F(i,j,\d x))_{i,j\in E}$ be a family of probability measures on $\mathbb{R}$. Consider a semi-markovian chain $(Y_n,X_n)$ on $\mathbb{R}\times E$ with transition probability $\widetilde{P}$, defined by $\widetilde{P}\Big((u,i),A\times\{j\}\Big)=\mathbb{P}(Y_{n+1}\in A,X_{n+1}=j|Y_n= u,X_n=i)=p_{i,j}F(i,j,A)$, for any $(u,i)\in\mathbb{R}\times E$, any Borel set $A\subset\mathbb{R}$ and any $j\in E$. We study the asymptotic behavior of the sequence of Laplace transforms of $(X_n,m_n)$, where $m_n=\min(S_0,S_1,...,S_n)$ and $S_n=Y_0+...+Y_{n-1}$. Under quite general assumptions on $F(i,j,dx)$, we prove that for all $(i,j)\in E\times E$, $\sqrt{n}\E_i[\exp(λm_n), X_n=j]$ converges to a positive function $H_{i,j}(λ)$ and we obtain further informations on this limit function as $λ\to 0^+$.