协方差的非齐次估计的可容许性

本文研究协方差的非齐次二次估计的可容许性,在平方损失下,我们给出了一个非齐次二次估计在非齐次二次估计类中是协方差的容许估计的充要条件.     

NS Condition of Admissibility for the Linear Estimator of Normal Mean with Unknown Variance

Suppose Y - N（β, σ^2 In）, where β ∈ R^n and σ^2 〉 0 are unknown. We study the admissibility of linear estimators of mean vector under a quadratic loss function. A necessary and sufficient condition of the admissible linear estimator is given.     

广义自回归模型参数的收缩估计

运用经典方法结合参数的先验信息得到了广义一阶自回归模型中自相关系数的收缩估计的闭式表达式,它是通常极大似然估计与先验均值的加权平均,在适当的先验信息下优于原来的估计.     

Ⅱ型区间删失数据下广义部分线性模型的筛极大似然估计(英文)

This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.     

基于边缘ZI回归模型的分组数据影响分析

为了研究含零较多的分组计数数据对边缘ZI(zero-inflated)回归模型的影响,在完全数据的对数似然基础上利用局部影响法对其进行诊断.同时,基于模型中参数的ES(expectation-solution)估计,分别研究了类内加权扰动、类音加权扰动、解释变量扰动和响应变量扰动下的局部影响分析,得到了相应的诊断统计量.最后,根据所得统计量,获得了园艺试验中一组计数数据的影响点,结果说明该文提出的方法是有效的.     

Bayes估计的中偏差下界

在某种正则条件下，对Bayes估计尾概率收敛速度问题进行了讨论。利用似然理论方法得到了Bayes估计的中偏差下界，从而改善了Bahadur型的收敛结果。     

WOD样本下密度函数核估计的强相合性

设（Xn,n≥1）是同分布的WOD随机变量序列,具有共同的密度函数f（x）,利用WUOD序列的指数不等式,在适当条件下获得了WOD样本下密度函数核估计的强相合性．     

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.     

Asymptotic analysis of a kernel estimator for parabolic stochastic partial differential equations driven by fractional noises

We study a strongly elliptic partial differential operator with time-varying coeffcient in a parabolic diagonalizable stochastic equation driven by fractional noises. Based on the existence and uniqueness of the solution, we then obtain a kernel estimator of time-varying coeffcient and the convergence rates. An example is given to illustrate the theorem.     

Deviation inequalities and moderate deviations for estimators of parameters in TAR models

In this paper, we establish some deviation inequalities and the moderate deviation principles for the least squares estimators of the parameters in the threshold autoregressive model under the assumption that the noise random variable satisfies a logarithmic Sobolev inequality.