时间序列模型L1-估计量的极限分布:平稳自回归模型

时间序列模型的L1-估计问题是非常重要的.这些估计量的许多性质都被研究过.然而,仍有一些基本问题有待解决.本文将给出平稳自回归模型L1-估计量的极限分布.     

时间序列模型L_1-估计量的极限分布:平稳自回旧模型

时间序列模型的L_1-估计问题是非常重要的．这些估计量的许多性质都被研究过．然而,仍有一些基本问题有待解决．本文将给出平稳自回归模型L_1-估计量的极限分布．     

时间序列模型L_1-估计量的极限分布:平稳自回旧模型

时间序列模型的L_1-估计问题是非常重要的．这些估计量的许多性质都被研究过．然而,仍有一些基本问题有待解决．本文将给出平稳自回归模型L_1-估计量的极限分布．     

关于AR（P）模型参数的LAD估计收敛性

一、引言和定理的叙述由于在实际问题中,具有无限方差的误差可能适合许多现象(参看[1]—[4]),所以估计下述自回归模型(1.1)的回归参数有重大的意义.     

用随机规划方法寻求线性回归模型小样本L1^—估计量的分布

为了使Ｌ１－估计能在ｉ导手械玫接τ这里我们给出求线性回归模型小样本Ｌ１－估计量的概率分布的方法。根据Ｌ１－估计量的特殊结构和使用随机规划的方法可以省去不少计算量，从而使这一方法更加有效。     

一类具有相依结构的离散时间风险模型的赤字分布

研究一类具有相依结构的离散时间风险模型的破产赤字问题.其中,保费和利率过程假设为两个不同的自回归移动平均模型.利用更新递归技巧,首先得到了该模型下破产赤字分布的递推公式.然后,根据该递推公式得到了赤字分布的上下界估计.     

基于EM算法的混合自回归滑动平均模型的参数估计

研究了一类用于时间序列建模的混合自回归滑动平均模型.该模型是由m个ARMA分量经过混合得到的,给出了混合自回归滑动平均模型参数估计的期望极大化(EM)算法,从而得到了混合系数和分量模型的参数,通过仿真说明了其有效性.     

相依MA(∞)误差下半参数模型小波估计的收敛速度(英文)

考虑半参数回归模型yi=xiβ+g(ti)+Vi(1≤i≤n), 其中(xi,ti)是已知的设计点, 斜率参数β是未知的, g(·)是未知函数, 误差Vi=sum from j=-∞ to ∞(cjei-j),sum from j=-∞ to ∞(|cj|∞)并且ei是负相关的随机变量. 在适当的条件下, 我们研究了β与g(·)小波估计量的强收敛速度. 结果显示g(·)的小波估计量达到最优收敛速度. 同时, 对β小波估计量也作了模拟研究.     

删失回归模型LAD估计的一些强的性质

删失回归模型是一种因变量受限制的模型, 也称为Tobit模型. 在较弱的条件之下, 给出了回归系数LAD估计的强相合性、强收敛速度及其Bahadur强表示.     

The limiting behavior of least absolute deviation estimators for threshold autoregressive models

The asymptotic behavior of the least squares (LS) estimators of the parameters in threshold autoregressive models has been completely studied in the literature. It is well known that in some cases the least absolute deviation (LAD) estimators are superior to the LS-estimators. This paper is devoted to studying the strong consistency and the asymptotic normality of the LAD-estimators in two cases where the threshold is known and/or unknown.     

Weighted least absolute deviations estimation for periodic ARMA models

This paper investigates the weighted least absolute deviations estimator (WLADE) for causal and invertible periodic autoregressive moving average (PARMA) models. Asymptotic normality of the estimator is derived under a fractional moment condition. A simulation study is given to assess the performance of the proposed WLADE.     

Least absolute deviation estimation for general fractionally integrated autoregressive moving average time series models

We introduce a new class of ARFIMA models, which removes the restrictions that the roots of AR and MA polynomials are outside the unit circle. We establish consistency and asymptotic normality of the least absolute deviation estimator under non-Gaussian setting.     

LIMITING BEHAVIOR OF UNIFORM RECURSIVE TREES

The authors consider the limiting behavior of various branches in a uniform recursive tree with size growing to infinity.The limiting distribution ofζ_(n,m),the number of branches with size m in a uniform recursive tree of order n,converges weakly to a Poisson distribution with parameter 1/m with convergence of all moments.The size of any large branch tends to infinity almost surely.     

Strong convergence of estimators in nonlinear autoregressive models

In the paper we prove rates of strong convergence of M-estimators for the parameters in a general nonlinear autoregressive model. In the proofs we utilize a variational principle from stochastic optimization theory which was proved by Shapiro (Ann. Oper. Res. 30 (1991) 169). The application of the general theory is illustrated in the case of continuous threshold models.     

A Berry-Esseen bound for least squares error variance estimators of regression parameters

We construct a precise Berry-Esseen bound for the least squares error variance estimators of regression parameters. Our bound depends explicitly on the sequence of design variables and is of the order O(N ^−1/2) if this sequence is “regular” enough. Supported by the Lithuanian State Science and Studies Foundation. Vilnius University, Naugarduko 24; Institute of Mathematics and Informatics, Akademijos 4, 2600 Vilnius, Lithuania. Published in Lietuvos Matematikos Rinkinys, Vol. 39, No. 1, pp. 1–8, January–March, 1999.     

On the asymptotic behavior of the parameter estimators for some diffusion processes: application to neuronal models

We consider a sample of i.i.d. times and we interpret each item as the first-passage time (FPT) of a diffusion process through a constant boundary. The problem is to estimate the parameters characterizing the underlying diffusion process through the experimentally observable FPT’s. Recently in Ditlevsen and Lánsky (Phys Rev E 71, 2005) and Ditlevsen and Lánsky (Phys Rev E 73, 2006) closed form estimators have been proposed for neurobiological applications. Here we study the asymptotic properties (consistency and asymptotic normality) of the class of moment type estimators for parameters of diffusion processes like those in Ditlevsen and Lánsky (Phys Rev E 71, 2005) and Ditlevsen and Lánsky (Phys Rev E 73, 2006). Furthermore, to make our results useful for application instances we establish upper bounds for the rate of convergence of the empirical distribution of each estimator to the normal density. Applications are also considered by means of simulated experiments in a neurobiological context.      

Asymptotic bias of the least squares estimator for multivariate autoregressive models

Summary The asymptotic bias of the least squares estimator for the multivariate autoregressive models is derived. The formulas for the low order univariate autoregressive models are given in terms of the simple functions of parameters. Our results are useful to the bias correction method of the least squares estimation. This work was supported by National Science Foundation Grant SES79-13976 at the Institute for Mathematical Studies in the Social Sciences, Stanford University. This paper is a revision of Discussion Paper No. 504, The Center for Mathematical Studies in Economics and Management Science, Northwestern University, October 1981.     

A review of hydrodynamical models for semiconductors: Asymptotic behavior

We review recent results on the hydrodynamical model for semiconductors. The derivation of the mathematical model from the semi-classical Boltzmann equation in terms of the moment method is performed, and the mathematical analysis of the asymptotic behavior of both classical solutions and entropy weak solutions is given on spatially bounded domain or whole space.Dedicated to Constantine Dafermos on his 60^th birthday     

Limiting behavior of the eigenvalues of a multivariate F matrix

The spectral distribution of a central multivariate F matrix is shown to tend to a limit distribution in probability under certain conditions as the number of variables and the degrees of freedom tend to infinity.