集团抽样不等概方法的均值估计及其性质

在集团抽样调查中，[1]和[2]给出了一般抽样方法及均值估计和它性质的论述，但未考虑各集团在总体中的地位对估计结果的影响，本文给出了集团抽样的不等概方法和均值的无偏估计，同时也给出了这种估计的方差及方差的无偏估计。     

后缀树的平均高度

本文利用概率论中的方法，探讨了由随机字符序列构成的后缀树，其高度Hn的概率特性，给出了均值E[Hn]和方差D[Hn]的较为精确的上下界估计。     

一种单因子异方差模型的分析方法

本文提出了一种单因子异方差模型,导出这种异方差分析方法,并给出了模型中均值与方差的估计.通过对仿真和数字化设计模型数据进行检验,结果表明,采用此方法比传统方法更有效.     

样本大小不断变化时如何计算均值和标准差?

问:在计算样本的均值和方差时,由于样本大小不断变化(增加或减少),每一次都要把全部数据代入公式计算。有没有这样的公式,它可以利用原来计算过的均值和方差值合并新增加(或减少)的几个数据而得到新的均值和方差?答:有。请记住以下两个公式:公式中x,s2为x1,…,x4的均值和方差;x     

正态总体均值与标准差比在简单半序约束下的最大似然估计

本文考虑两个正态总体均值和方差是未知参数,均值与标准差的比在简单半序约束下,且样本个数不同时,计算均值和标准差的最大似然估计的问题.给出了计算均值和标准差的最大似然估计的方法.同时还给出了三个总体均值与标准差的比在简单半序约束下求均值和标准差的最大似然估计的计算方法,并给出了迭代算法的模拟结果.     

相关系数平稳序列的均值和方差的参数估计

文献[1]提出了相关系数平稳过程并讨论了它的参数估计方法,其参数估计值采用了数值迭代法.本文在[1]的基础上对一种特殊的相关系数平稳序列的均值和方差提出一种具体的解决方法,得到了确切的均值和方差的参数估计表达式.     

U统计量均值的固定长度的序贯区间估计

Y.S Chow 和 H.Robbins[1]讨论了一般总体方差σ~2。未知时均值μ的固定长度2d,给定置信概率1-α的序贯区间估计.此后一些作者又作了进一步的研究,其中Sproule[2]曾给出了 U 统计量均值的固定长度的序贯区间估计.本文主要讨论 U 统计量均值θ的固定长度的刻度型序贯区间估计.并顺便讨论一种构作 U 统计量均值固定长度区间估计的渐近有效两阶段抽样方案.     

人寿保险中随机Thiele微分方程的另一方法及应用

本文针对人寿保单被描述为时间非时齐的马氏链情形,较之文[7]更一般的假设条件下,给出了鞅M(t)=E[V0│Ft]的局部平方可积鞅的表示性,该方法不同于文[4]的方法。由此得到了随机Thiele微分方程,而且给出损失方差的一般表示。文章最后通过赔偿依赖于准备金的寡妇养老 金例子说明了随机Thiele微分方程的应用。     

关于任意边界缺口或裂纹群问题的一类解法——(Ⅲ)边界裂纹群的计算*

本文是文献[1]、[2]关于任意边界缺口或裂纹群问题的一类解法研究的继续。这里我们利用和发展了文献[1]、[2]所提出的理论和计算公式,对边界裂纹群问题进行了实际计算.数值计算实例表明:本文所给出的方法在特征参数适当的范围内是行之有效的.本文的结果扩充了“应力强度因子手册”中的工作.     

负超几何随机变量期望与方差的一种简捷计算

给出了负超几何分布的概率模型,通过将负超几何分布随机变量进行和式分解,比较简捷地计算了它的期望和方差,并指出文献[4]计算的期望和方差是错误的.     

Blind SNR estimation for limited time series

A blind SNR estimation method for discrete data is presented. The original noise-free data is assumed to follow a known signal model with an unknown attenuation. The SNR in noisy data is estimated using a polynomial fit obtained from the correlative characteristics between SNR and variance fractal dimension values. The bias and the “standard error” (root mean square error) of the estimator are used as performance measures. The simulated performance of the estimator for a specific signal model with real additive white Gaussian noise assumption is compared to that of a published decision-aided (nonblind) SNR estimator.     

Variation analysis of uncertain stationary independent increment processes

A stationary independent increment process is an uncertain process with stationary and independent increments. This paper aims to calculate the variance of stationary independent increment processes, and gains that, for each fixed time, the variance is a constant multiplying the square of time. Based on this result, it is proved that the total variation of stationary independent increment process with finite variance is bounded almost surely. Besides, the quadratic variation of stationary independent increment process with finite variance is 0 almost surely and in mean.     

两水平无重复因析试验散度效应的估计

本文对两水平无重复因析试验给出了散度效应的一种新的估计,称为AMH估计,改进了文献中散度效应的较好的MH估计的一个缺陷,给出并证明了AMH估计的无偏条件,证明了AMH估计比MH估计有更小的方差下界.最后通过模拟试验比较了AMH和MH估计的偏度,方差和均方误差.     

Variance comparison between infinitesimal perturbation analysis and likelihood ratio estimators to stochastic gradient

We theoretically compare variances between the Infinitesimal Perturbation Analysis (IPA) estimator and the Likelihood Ratio (LR) estimator to Monte Carlo gradient for stochastic systems. The results presented in Cui et al. (2020) [2] on variance comparison between these two estimators are substantially improved. We also prove a practically interesting result that the IPA estimators to European vanilla and arithmetic Asian options' Delta, respectively, have smaller variance when the underlying asset's return process is independent with the initial price and square integrable.     

Calculating the variance of errors in population forecasting

For judging the quality of population forecasting methods, the root mean square error (RMSE) has proven to be a useful tool. A suitable variance component model is proposed for calculating the variance of the RMSE. The method is demonstrated by an example.     

Smoothed estimates for models with random coefficients and infinite variance innovations

Infinite variance processes have attracted growing interest in recent years due to its applications in many areas of statistics (see [1] and references therein). For example, ARIMA time-series models with infinite variance innovations are widely used in financial modelling. However, a little attention has been paid to incorporate infinite variance innovations for time-series models with random coefficients introduced by [2]. This paper considers the problem of nonparametric estimation for some time-series models using the smoothed least absolute deviation (SLAD) estimating function approach. We introduce a class of kernels in order to smooth the LAD estimators. It is also shown that this new SLAD estimators are superior than some existing ones.     

Optimal variance estimation based on lagged second-order difference in nonparametric regression

Differenced estimators of variance bypass the estimation of regression function and thus are simple to calculate. However, there exist two problems: most differenced estimators do not achieve the asymptotic optimal rate for the mean square error; for finite samples the estimation bias is also important and not further considered. In this paper, we estimate the variance as the intercept in a linear regression with the lagged Gasser-type variance estimator as dependent variable. For the equidistant design, our estimator is not only \(n^{1/2}\)-consistent and asymptotically normal, but also achieves the optimal bound in terms of estimation variance with less asymptotic bias. Simulation studies show that our estimator has less mean square error than some existing differenced estimators, especially in the cases of immense oscillation of regression function and small-sized sample.     

Optimization Strategies for Optimization Procedures and Algorithms

New theory of circle patterns (or packing) is now well developed. Uniformization result, the circle packing theorem [1], is based on analytic functions, while Doyle spirals are analoques of entire functions. Doyle constructed entire immersed hexagonal circle patterns analoques to the exponential map and conjectured that these immersed packings which came to be known as Doyle spirals, are the only entire immersed hexagonal circle packings [2, 3]. Schramm had found that a framework of circle patterns based on the square grid which is called SG patterns, is more tractable than the traditional hexagonal patterns [4]. We present a new approach concerned with a theory of discrete multistage optimization [5, 6]. Optimization strategies for optimization procedures are good tools for modelling. Consequently calculations are not far from empirical observations and experimental results. This clearly demonstrates the broad interest in our field and the role which mathematical modelling and analysis is expected to play with regard to the solution of the challenging problems that we are facing at the beginning of this millennium.     

线性模型参数向量的近似贝叶斯估计

用线性贝叶斯方法去同时估计线性模型中回归系数和误差方差,并在不知道先验分布具体形式的情况下,得到了线性贝叶斯估计的表达式.在均方误差矩阵准则下,证明了其优于最小二乘估计和极大似然估计.与利用MCMC算法得到的贝叶斯估计相比,线性贝叶斯估计具有显式表达式并且更方便使用.对于几种不同的先验分布,数值模拟结果表明线性贝叶斯估...     

线性混合效应模型中方差分量的估计

本文首先研究了含三个方差分量的线性混合随机效应模型改进的ANOVA估计, 此估计在均方损失下一致优于ANOVA估计. 由于这些方差估计取负值的概率大于零, 对得到的估计在某非负点采用截尾的方法得到非负估计是一种常用的方法. 对文章中提出的估计, 研究了此估计在某非负点截尾之后得到的估计在均方损失意义下优于截尾之前的估计的充分条件, 同时给出ANOVA估计在截尾之后优于它本身的充分条件, 而且将得到的结论推广到更一般的线性混合随机效应模型.