Integrated squared error of kernel-type estimator of distribution function

Let X ₁ ,...,X _n be a random sample drawn from distribution function F(x) with density function f(x) and suppose we want to estimate X(x). It is already shown that kernel estimator of F(x) is better than usual empirical distribution function in the sense of mean integrated squared error. In this paper we derive integrated squared error of kernel estimator and compare the error with that of the empirical distribution function. It is shown that the superiority of kernel estimators is not necessarily true in the sense of integrated squared error.     

MSE's of prediction in growth curve model with covariance structures

We consider the growth curve model with covariance structures: positive-definite, uniform covariance structure and serial covariance structure. Two types of prediction problems are studied in this paper. One is called the conditional prediction problem and the other is called the extended prediction problem. For both types of prediction problems, the mean squared error for a serial covariance structure is obtained for the estimates based on the conditional expectation: the mean squared error for an unrestricted covariance structure is compared with the mean squared error for a uniform covariance structure or a serial covariance structure. These results are exemplified by two sets of real data.This research was supported in part by Grant-in-Aid for general Scientific Research, The Ministry of Education, Science and Culture under Contract Number 03640239.     

Control of a hybrid conditionally linear Gaussian process

A control problem is considered where the coefficients of the linear dynamics are functions of a noisily observed Markov chain. The approximation introduced is to consider these coefficients as functions of the filtered estimate of the state of the chain; this gives rise to a finite-dimensional conditional Kalman filter. A minimum principle and a new equation for an adjoint process are obtained.This research was partially supported by NSERC under Grant A-7964, by the US Air Force Office of Scientific Research under Contract AFOSR-86-0332, and by the US Army Research Office under Contract DAAL03-87-0102.The authors obtained these results during a visit to UCSD by the first author in January 1990. This author wishes to thank Professor D. D. Sworder and his department for their hospitality.     

一类半线性反应对流扩散模型的特征差分方法和分析

1.引 言如下形式的半线性反应对流扩散方程组分别在生命科学、化学和环境科学中,有大量的应用模型[1-3].其中文献[2-6]分别讨论了方程组(1.1)的各种特殊模型的定性性质.文献[6]讨论了一类线性模型的流线扩散有限元分析.作者在文[7]中,分别利用标准有限元方法和交替方向有限元方法,对(1.1)的一些特殊情形作了数值分析.     

不可压N-S方程的差分流线扩散法

In this paper, a Finite-Difference Streamline-Diffusion (FDSD as short) scheme for the incompressible Navier-Stokes Equations is constructed with linear finite element spaces. The analysis showes that the scheme considered has good stability and higher accuracy than the standard finite elemnet method.     

舰船信息融合工程模型部分测试模型研究

通过对误差方差不等性检验 ,趋势函数线性性检验 ,从而得到误差随机部分的统计结果 ,建立起舰船信息融合工程模型的误差模型 .     

KdV方程对称李代数的伴随表示及其killing型

本文利用群的开拓理论得出了 Kd V方程对称李代数的伴随表示及其 Killing型 .     

基于GARCH模型族的中国股市波动性预测

收益与风险历来都是投资者与研究者所关注的问题 .本文选取 GA RCH、TGARCH和 EGARCH模型来拟合中国股市的波动性 .实证分析结果表明 ,中国股市的波动具有显著的波动聚类性与持续性 ;由 E-GARCH模型所预测的上证 30指数、上证综合指数和深证成份指数未来一天的波动要明显优于 GARCH和TGARCH模型的对应值 ,而对香港恒生指数 ,三种模型的预测结果无显著的差异 .     

美式期权定价中非局部问题的有限元方法

在本文中 ,我们关心的是美式期权的有限元方法 .首先 ,根据 [4 ]我们对所讨论的问题引进一个新奇的实用的方法 ,它涉及到对原问题重新形成准确的数学公式 ,使得数值解的计算可以在非常小的区域上进行 ,从而该算法计算速度快精度高 .进而 ,我们利用超逼近分析技术得到了有限元解关于 L2 -模的最优估计 .     

广义Benjamin-Bona-Mahony方程Fourier谱方法的大时间误差估计

§ 1　ＩｎｔｒｏｄｕｃｔｉｏｎＴｈｅＢｅｎｊａｍｉｎ Ｂｏｎａ Ｍａｈｏｎｙｅｑｕａｔｉｏｎｕｔ+ｕｘ+ｕｕｘ -ｕｘｘ-ｕｘｘｔ =0 ( 1 .1 )ｉｎｃｏｒｐｏｒａｔｅｓｎｏｎｌｉｎｅａｒｄｉｓｐｅｒｓｉｖｅａｎｄｄｉｓｓｉｐａｔｉｖｅｅｆｆｅｃｔｓ ,ａｎｄｈａｓｂｅｅｎｐｒｏｐｏｓｅｄａｓａｍｏｄｅｌｆｏｒｂｏｔｈｔｈｅｂｏｒｅｐｒｏｐａｇａｔｉｏｎａｎｄｔｈｅｗａｔｅｒｗａｖｅｓ[1,2 ] .Ｔｈｅｅｘｉｓｔｅｎｃｅａｎｄｕｎｉｑｕｅｎｅｓｓｏｆｓｏｌｕｔｉｏｎｓｆｏｒｔｈｉｓｅｑｕａｔｉｏｎｈａｖｅｂｅｅ…