Reverse processes and some limit theorems of multitype Galton-Watson processes

We classify the reverse process {X_n} of a multitype Galton-Watson process {Z_n}. In the positive recurrent cases we give the stationary measure for {X_n} explicitly, and in the critical case, supposing that all the second moments of Z₁ are finite, we establish the convergence in law to a gamma distribution. Limit distributions of {Z_cn}, 0 < c < 1, conditioned on Z_n, are also given in the subcritical, supercritical and critical cases, respectively. These extend the previous one-type work of W. W. Esty.     

On the reverse process of a critical multitype Galton-Watson process without variances

Limit theorems are established for the reverse process of a critical multitype Galton-Watson process without variances. These results are an extension of those of Nakagawa |5| in the case with finite variances.     

Galton-Watson分支过程的谱半径的概率刻画

谱半径是不可约马尔可夫链的一个很重要的特征数字.Galton-Watson分支过程是一类特殊的马尔可夫链,我们已经证明了在不可约的条件下,Galton-Watson分支过程的谱半径等于其对应的概率母函数f(s)在灭绝概率q点的导数值.本文主要从理论上刻画从过程的任何状态逃离速度的Galton-Watson分支过程的谱半径的概率意义.     

Central limit theorems for supercritical branching Markov processes

In this paper we establish spatial central limit theorems for a large class of supercritical branching Markov processes with general spatial-dependent branching mechanisms. These are generalizations of the spatial central limit theorems proved in [1] for branching OU processes with binary branching mechanisms. Compared with the results of [1], our central limit theorems are more satisfactory in the sense that the normal random variables in our theorems are non-degenerate.     

Local Power Properties of Kernel Based Goodness of Fit Tests

If (X_i, i ) is a strictly stationary process with marginal density function f, we are interested in testing the hypothesis H₀: {f=f₀}, where f₀ is given. We consider different test statistics based on integrated quadratic forms measuring the proximity between f_n, a kernel estimator of f, and f₀, or between f_n and its expected value computed under H₀. We study the asymptotic local power properties of the testing procedures under local alternatives. This study generalizes to the multidimensional case in a context of dependence the corresponding one made by P. J. Bickel and M. Rosenblatt in 1973 (Ann. Statist.1, 1071–1095).     

关于拟合优度检验的EDF统计量的若干评注(英语)

拟合优度检验是建立统计模型的一个重要手段,很多检验统计量用一个理想样本能达到它们自己的极值,但EDF统计量做不到,这无疑会影响检验的势。在本文中,我们将提出某些调整型EDF统计量,它们具有这些性质,并改进了EDF检验,蒙得卡罗模拟表明,调整型EDF统计量在很多场合要必EDF具有更高的势,特别对重尾的备选分布更是这样,我们还考察了检验的形态与它们的极值点之间的关系。     

Further results on convergence and stability of a generalization of the Richardson extrapolation process

In an earlier paper by the author a detailed convergence and stability analysis of a generalization of the Richardson extrapolation process was given under certain conditions. In the present work these conditions are modified and relaxed considerably, and results are obtained on convergence and stability under the new conditions. As the previous ones, these new results are asymptotic in nature, and contain the former. The conditions of the present paper are naturally satisfied, e.g., by the trapezoidal rule approximations of finite range integrals of functions having algebraic and logarithmic end point singularities.     

Limit theorems for the integral of a population process with immigration

A limit theorem is proven for the integral of a general class of population processes possessing independent immigration components. For the special case of the Bellman-Harris process with immigration, further results are obtained.     

On a property related to convergence in probability and some applications to branching processes

It is shown that any real-valued sequence of random variables {X_n} converging in probability to a non-degenerate, not necessarily a.s. finite limit X possesses the following property: for any c with P(X? (c ? δ, c + δ)) > 0 for all δ > 0, there exists a sequence {c_n} with lim_n→∞ c_n = c such that for any ε > 0, lim_n→∞ P(Xδ (c ? ε, c + ε) |X_n = c_n) = 1. This property is applied to various types of branching processes where $\text{X}{}_{\mathrm{n}}\text{=}\text{Z}{}_{\mathrm{n}}\text{C}{}_{\mathrm{n}}$ or $\text{X}{}_{\mathrm{n}}\text{=}\text{U(Z}{}_{\mathrm{n}}\text{)}\text{C}{}_{\mathrm{n}}\text{{C}{}_{\mathrm{n}}\text{}}$ being a sequence of constants or random variables and U a slowly varying function. If {Z_n} is a supercritical branching process in varying or random environment, X is shown to have a continuous and strictly increasing distribution function on (0, ∞). Characterizations of the tail of the liniting distribution of the finite mean and the infinite mean supercritical Galton-Watson processes are also obtained.     

Prediction and Classification of Non-stationary Categorical Time Series

Partial likelihood analysis of a general regression model for the analysis of non-stationary categorical time series is presented, taking into account stochastic time dependent covariates. The model links the probabilities of each category to a covariate process through a vector of time invariant parameters. Under mild regularity conditions, we establish good asymptotic properties of the estimator by appealing to martingale theory. Certain diagnostic tools are presented for checking the adequacy of the fit.     

Central limit theorems for a supercritical branching process in a random environment

For a supercritical branching process (Z_n) in a stationary and ergodic environment ξ, we study the rate of convergence of the normalized population W_n=Z_n/E[Z_n|ξ] to its limit W_∞: we show a central limit theorem for W_∞−W_n with suitable normalization and derive a Berry-Esseen bound for the rate of convergence in the central limit theorem when the environment is independent and identically distributed. Similar results are also shown for W_n+k−W_n for each fixed k∈N^∗.     

Bootstrapping the empirical distribution of a linear process

The validity of the moving block bootstrap for the empirical distribution of a short memory causal linear process is established under simple conditions that do not involve mixing or association. Sufficient conditions can be expressed in terms of the existence of moments of the innovations and summability of the coefficients of the linear model. Applications to one and two sample tests are discussed.     

Some convergence theorems of non-implicit iteration process with errors for a finite families of I-asymptotically nonexpansive mappings

The purpose of this paper is to study the weak and strong convergence of non-implicit iteration process with errors to a common fixed point for a finite family of I-asymptotically quasi-nonexpansive mappings in Banach spaces. The results presented in this paper extend and improve the corresponding results of several authors [1], [2], [7], [8], [9], [10], [11], [12], [13], [14], [17], [19], [22], [23], [24], [25], [26], [27], [28] and [29].     

Asymptotic analysis of flow in wavy tubes and simulation of the extrusion process

The paper is devoted to the mathematical modelling of an extrusion process. Usually, an extruder has a very complicated geometry. This generates a lot of difficulties for computations of three‐dimensional flows. In the present paper, we develop and justify the asymptotic domain decomposition strategy in order to parallelize the computational process and reduce the memory. The error estimates are proved for the Stokes steady‐state equation in the two‐dimensional and three‐dimensional cases. Then, the asymptotic domain decomposition procedure is applied for numerical testing and computations of the non‐Newtonian fluid simulating a real process of the polymer extrusion. Copyright © 2007 John Wiley & Sons, Ltd.     

Weak convergence of random polygonal lines to a Gaussian process

We obtain a limit theorem of convergence in distribution for random polygonal lines defined by sums of independent random variables with replacements. In a particular case, the limit is the Gaussian Ornstein-Uhlenbeck process.__________Translated from Lietuvos Matematikos Rinkinys, Vol. 45, No. 1, pp. 33–44, January–March, 2005.Translated by V. Mackeviius     

On total progeny of multitype Galton-Watson process and the first passage time of random walk on lattice

In this paper,we form a method to calculate the probability generating function of the total progeny of multitype branching process.As examples,we calculate probability generating function of the total progeny of the multitype branching processes within random walk which could stay at its position and(2-1) random walk.Consequently,we could give the probability generating functions and the distributions of the first passage time of corresponding random walks.Especially,for recurrent random walk which could stay at its position with probability 0 r 1,we show that the tail probability of the first passage time decays as 2/(π(1-r)~(1/2)) n~(1/1)= when n →∞.     

On a 1D transport equation with nonlocal velocity and supercritical dissipation

We study a 1D transport equation with nonlocal velocity. First, we prove eventual regularization of the viscous regularization when dissipation is in the supercritical range with non-negative initial data. Next, we will prove global regularity for solutions when dissipation is slightly supercritical. Both results utilize a nonlocal maximum principle.     

Some ergodic theorems for a parabolic Anderson model

In this paper,we study some ergodic theorems of a class of linear systems of interacting diffusions,which is a parabolic Anderson model.First,under the assumption that the transition kernel a=(a(i,j)) i,j∈s is doubly stochastic,we obtain the long-time convergence to an invariant probability measure νh starting from a bounded a-harmonic function h based on self-duality property,and then we show the convergence to the invariant probability measure νh holds for a broad class of initial distributions.Second,if(a(i,j)) i,j∈S is transient and symmetric,and the diffusion parameter c remains below a threshold,we are able to determine the set of extremal invariant probability measures with finite second moment.Finally,in the case that the transition kernel(a(i,j)) i,j∈S is doubly stochastic and satisfies Case I(see Case I in [Shiga,T.:An interacting system in population genetics.J.Math.Kyoto Univ.,20,213-242(1980)]),we show that this parabolic Anderson model locally dies out independent of the diffusion parameter c.     

General theorems on rates of convergence in distribution of random variables I. General limit theorems

Let (X_n)_n?$\text{N}$ be a sequence of real, independent, not necessarily identically distributed random variables (r.v.) with distribution functions F_Xn, and S_n = Σ_i=1ⁿX_i. The authors present limit theorems together with convergence rates for the normalized sums ?(n)S_n, where ?: $\text{N}$ → $\text{R}$⁺, ?(n) → 0, n → ∞, towards appropriate limiting r.v. X, the convergence being taken in the weak (star) sense. Thus higher order estimates are given for the expression ∝_$\text{R}$f(x) d[F_?(n)Sn(x) ? F_X(x)] which depend upon the normalizing function ?, decomposability properties of X and smoothness properties of the function f under consideration. The general theorems of this unified approach subsume O- and o-higher order error estimates based upon assumptions on associated moments. These results are also extended to multi-dimensional random vectors.