广义线性模型中方差函数的非参数估计

本文提出了$\sigma(u)$的一种改进的估计$\wh\sigma_n(u)$, 在一定的条件下证明了$\sup\limits_{u}|\wh\sigma_n(u)-\sigma(u)|$相对于[1]中的估计以更快的速度依概率收敛于0, 并修正了定义区间.     

广义线性模型中方差函数的非参数估计

本文提出了$\sigma(u)$的一种改进的估计$\wh\sigma_n(u)$, 在一定的条件下证明了$\sup\limits_{u}|\wh\sigma_n(u)-\sigma(u)|$相对于[1]中的估计以更快的速度依概率收敛于0, 并修正了定义区间.     

带有干扰噪声的自回归过程的参数估计及渐近性质

本文讨论了带有干扰噪声的自回归过程Y(t)=X(t)+Z(t),其中X(t)为AR(p)序列,Z(t)、为干扰噪声序列的未知参数φ_1,…,φ_p,σ_g~2,σ_x~2,谱密度f(λ)的估计问题,对所给估计建立了强收敛速度和渐近正态性,并给出了模型阶的一种强相容估计方法。     

系统风险Beta系数的非参数估计

关于系统风险（Ｂｅｔａ系数）对时间的非平稳性已得到广泛的认可,但对Ｂｅｔａ系数的估计方法大都是在平稳的（或局部平稳）前提下,或者事先给定Ｂｅｔａ系数一个主观参数模型下进行的。本文假定证券价格或收益是一个连续时间的随机过程（时齐Ｉｔｏ过程）,给出了过程的均值函数和协方差函数的同时非参数估计,把此结果应用到Ｂｅｔａ系数上,得到了Ｂｅｔａ系数的非参数估计。     

有限个广义渐近非扩张映射具误差的复合隐迭代过程

在一致凸Banach空间研究了一个新的有限个广义渐近非扩张映射具误差的复合隐迭代过程.利用空间满足Opial条件和算子满足半紧性条件,我们证明了这个隐迭代过程强、弱收敛于有限个广义渐近非扩张映射的公共不动点.这些结果是目前所得成果的完善和推广.     

一般截断族中半参数估计的渐近效率

本文表述了线性参数函数的估计在渐近中位无偏限制下的渐近效率的定义,研究了一般截断型分布族 f(x;θ_1,θ_2)I(θ_1≤x≤θ_2)dx,为函数 c_1θ_1+c_2θ_2构造了一类直观合理的渐近中位无偏的半参数估计,算出了它们的渐近效率,发现了一种有趣现象:效率仅与比值 c_1f(θ_2;θ_1,θ_2)/c_2f(θ_1;θ_1,θ_2)有关;并且当 c_1c_2≥0时,这些估计是渐近有效的,而当 c_1c_2<0时,效率在1与0.7412之间,极小值是0.7412.     

风险中性过程的非参数估计

无套利定价理论说明，任何衍生证券定价都可以在基础资产价格(或收益)的风险中性过程基础上进行，而且方差函数的估计是估计风险中性过程的关键问题，由于方差不可观测，采用特定的参数模型将是危险的。本文主要讨论时间序列模型下条件方差函数的非参数估计，对核估计和局部多项式估计给出确定窗宽的M-图方法，并给出时间序列模型下衍生证券定价的风险中性调整方法，最后作了模拟计算。     

一类广义离散双险种风险模型

本推广了[1]中的离散双险种风险模型，讨论了保单到达过程为Poisson随机序列时的情况，得到了最终破产概率的Lundberg不等式以及一般表达式。     

异方差的线性混合模型的参数估计

本文研究既含有固定效应又含有随机效应的线性混合模型,在随机效应的方差不同即异方差情况下,即考虑方差受外界因素的影响,如温度、湿度等,我们称之为协变量,在有协变量情况下对方差建立对数线性模型,运用最大似然估计讨论了固定效应的估计和随机效应的预测,并且用约束最大似然(REML)方法研究对数线性模型中参数和随机误差中参数(离差参数)的估计,并讨论估计量的性质及离差参数估计量的渐近正态性。     

Bayesian Nonparametric Analysis for a Generalized Dirichlet Process Prior

This paper considers a generalization of the Dirichlet process which is obtained by suitably normalizing superposed independent gamma processes having increasing integer-valued scale parameter. A comprehensive treatment of this random probability measure is provided. We prove results concerning its finite-dimensional distributions, moments, predictive distributions and the distribution of its mean. Most expressions are given in terms of multiple hypergeometric functions, thus highlighting the interplay between Bayesian Nonparametrics and special functions. Finally, a suitable simulation algorithm is applied in order to compute quantities of statistical interest.     

非参数计量经济联立模型的局部线性两阶段最小二乘估计

联立方程模型在经济政策制定，经济结构分析和经济预测方面起重要作用，本在随机设计（模型中所有变量为随机变量）下，提出了非参数计量经济联立模型的局部线性两阶段最小二乘估计并利用概率论中大数定理和中心极限定理在内点处研究了它的大样本性质，证明了它的一致性和渐近正态性，它在内点处的收敛速度达到了非参数函数估计的最优收敛速度。     

Generalized Chi Square Method for the Estimation of Weights

This paper presents a generalized chi square method for the estimation of weights in the analytic hierarchy process and studies its desirable properties such as invariance under transpose, harmony, and rank preservation. A convergent iterative algorithm is given and its practicality is shown by a numerical example.     

一类非参数回归模型核估计的渐近性质

研究一类新的非参数回归模型回归函数的核估计问题,其中误差项为一阶非参数自回归方程.通过重复利用Watson-Nadaraya核估计方法,构造了回归函数及误差回归函数的估计量分别为m(.)和ρ(.),在适当的条件下,证明了估计量m(.)和ρ(.)的渐近正态性.     

Nonparametric Estimation of Regression Functions in Point Process Models

We prove that the empirical L ²-risk minimizing estimator over some general type of sieve classes is universally, strongly consistent for the regression function in a class of point process models of Poissonian type (random sampling processes). The universal consistency result needs weak assumptions on the underlying distributions and regression functions. It applies in particular to neural net classes and to radial basis function nets. For the estimation of the intensity functions of a Poisson process a similar technique yields consistency of the sieved maximum likelihood estimator for some general sieve classes. This revised version was published online in August 2006 with corrections to the Cover Date.     

线性过程误差下的非参数回归模型的注记

对于非参数回归模型Yni=g(xni)+εni,1in,用一般非参数方法,定义了未知函数g(.)的估计量gn(x),当误差序列{εni,1in}为一弱平稳线性过程序列时,在一定条件下,获得了估计量gn(x)的一致强相合性.     

一类非线性广义Euler-Poisson-Darboux方程整体解的渐近性质

J. L Lions and W. A. Stranss [1] have proved the existence of a global solution of the initial boundary value problem for nonlinear generalized Euler-Possion-Darboux equation. In this paper we are going to investigate the asymptotic behavior of the global solution by a difference inequality.     

一类线性回归模型中核估计的渐近性质

考虑固定设计下具有一阶非参数自回归误差的线性模型,构造了参数和非参数函数的N-W核估计,在适当的条件下,证明了参数估计的强相合性,同时给出了非参数函数估计的渐近正态性.     

Nonparametric Density Estimation for a Long-Range Dependent Linear Process

We estimate the marginal density function of a long-range dependent linear process by the kernel estimator. We assume the innovations are i.i.d. Then it is known that the term of the sample mean is dominant in the MISE of the kernel density estimator when the dependence is beyond some level which depends on the bandwidth and that the MISE has asymptotically the same form as for i.i.d. observations when the dependence is below the level. We call the latter the case where the dependence is not very strong and focus on it in this paper. We show that the asymptotic distribution of the kernel density estimator is the same as for i.i.d. observations and the effect of long-range dependence does not appear. In addition we describe some results for weakly dependent linear processes.     

误差为鞅差序列的非参数回归模型的完全相合性

在误差为鞅差序列的条件下,利用截尾方法及鞅差序列的指数不等式,研究了非参数回归模型P-C估计量的完全收敛性,且得到了完全收敛的收敛速度.