因变量缺失下部分线性可加模型的估计和检验

本文研究部分线性可加模型在因变量存在缺失情形下的统计推断问题. 首先基于完整数据方法提出了参数分量的Profile 最小二乘估计并证明估计量的渐近正态性. 为了给出参数分量的区间估计,构造了渐近分布为卡方分布的经验似然统计量. 为了检验参数分量的线性约束条件, 构造了调整的广义似然比检验统计量, 当原假设成立时其渐近分布为卡方分布,从而将广义似然比检验推广到了缺失数据情形. 最后通过数值模拟验证所提方法的有效性.     

因变量缺失下部分线性可加模型的估计和检验（英文）

本文研究部分线性可加模型在因变量存在缺失情形下的统计推断问题.首先基于完整数据方法提出了参数分量的Profile最小二乘估计并证明估计量的渐近正态性.为了给出参数分量的区间估计,构造了渐近分布为卡方分布的经验似然统计量.为了检验参数分量的线性约束条件,构造了调整的广义似然比检验统计量,当原假设成立时其渐近分布为卡方分布,从而将广义似然比检验推广到了缺失数据情形.最后通过数值模拟验证所提方法的有效性.     

部分线性变系数模型的Profile Lagrange乘子检验

对于部分线性变系数模型附有约束条件时的估计与检验问题,基于Profile最小二乘方法给出了参数部分以及非参数部分的约束估计并研究了它们的渐近性质,并针对约束条件构造了Profile Lagrange乘子检验统计量,证明了该统计量在原假设下的渐近分布为χ2分布,从而将Lagrange乘子检验方法推广到了半参数模型上.     

一类多元重复测量模型参数的似然比检验及其功效分析

在多元重复测量试验模型下,当受试对象观测矩阵的协方差矩阵∑为等方差等协方差结构时,给出了参数的似然比检验统计量.给出该检验在原假设下的渐近零分布和在备择假设下的渐近非零分布,并就检验的功效进行了分析.     

重复测量试验模型参数似然比检验及其功效分析

本文给出了在重复测量试验模型下, 当受试对象观测向量的协方差矩阵$\Sigma$为复合对称阵时,参数的似然比检验统计量; 给出该检验在原假设下的渐近零分布和在备择假设下的渐近非零分布;并就其功效进行了分析.     

广义空间模型的方差齐性检验

研究了广义空间模型的方差齐性检验问题,在异方差情形下导出了Score检验统计量的具体形式和近似分布.分别应用于混合空间自回归模型和空间误差模型,给出了相应的检验统计量和渐近分布.并利用Monte Carlo模拟对检验统计量的性质进行分析.最后,通过中国能源利用效率的区域特征数据证明了方法的有效性.     

一阶自相关结构下椭球线性混合效应模型的约束推断（英文）

本文在椭球分布族下研究一阶自相关线性混合效应模型的约束极大似然估计问题.分别考虑位置参数在等式和不等式线性约束这两种情况下的极大似然估计值.同时对约束条件下的兴趣参数给出三种渐近等价的检验方法.蒙特卡洛模拟说明本文方法的有效性和稳健性.本文结合模型对Framingham心脏研究中的胆固醇水平进行了分析.     

信用传染违约Aalen加性风险模型

本文考虑了基于加性风险模型的信用风险违约预报模型,不但考虑了宏观因素和公司个体因素,并且通过引入行业因素来刻画公司间可能存在的不同于宏观因素的信用传染效应,由此克服了以往模型对违约相关性的低估.本文在参数加性风险模型下给出极大似然估计及渐近性,提出两种估计方法并比较二者表现,得到最优权估计更加有效.同时本文还考虑了半参数的风险模型,并基于鞅的估计方程得到其估计及渐近性,均得到不错的结果.     

有限混合Laplace分布回归模型局部估计的EM算法

本文研究了一类有限混合Laplace分布回归模型的局部极大似然估计问题. 利用核回归方法和最大化局部加权似然函数的EM算法, 获得了参数函数的局部极大似然估计量, 并讨论了它们的渐近偏差, 渐近方差和渐近正态性. 推广了有限混合回归模型下局部非参数估计的结果.     

多类型复发间隔时间下广义半参数风险模型

本文在多类型复发间隔时间数据下,研究了一类广义半参数风险回归模型的参数估计问题,给出了该模型中未知参数和非参数函数的一种估计方法,并证明了估计量的相合性和渐近正态性.最后利用数值模拟来评估估计量在有限样本下的表现.     

Maximum likelihood estimate for discontinuous parameter in stochastic hyperbolic systems

The purpose of this paper is to study the identification problem of a spatially varying discontinuous parameter in stochastic hyperbolic equations. In previous works, the consistency property of the maximum likelihood estimate (MLE) was explored and the generating algorithm for MLE proposed under the condition that an unknown parameter is in a sufficiently regular space with respect to spatial variables.In order to show the consistency property of the MLE for a discontinuous coefficient, we use the method of sieves, i.e. the admissible class of unknown parameters is projected into a finite-dimensional space. For hyperbolic systems, we cannot obtain a regularity property of the solution with respect to a parameter. So in this paper, the parabolic regularization technique is used. The convergence of the derived finite-dimensional MLE to the infinite-dimensional MLE is justified under some conditions.     

Statistical inference in constrained latent class models for multinomial data based on $$\phi $$-divergence measures

In this paper we explore the possibilities of applying \(\phi \)-divergence measures in inferential problems in the field of latent class models (LCMs) for multinomial data. We first treat the problem of estimating the model parameters. As explained below, minimum \(\phi \)-divergence estimators (M\(\phi \)Es) considered in this paper are a natural extension of the maximum likelihood estimator (MLE), the usual estimator for this problem; we study the asymptotic properties of M\(\phi \)Es, showing that they share the same asymptotic distribution as the MLE. To compare the efficiency of the M\(\phi \)Es when the sample size is not big enough to apply the asymptotic results, we have carried out an extensive simulation study; from this study, we conclude that there are estimators in this family that are competitive with the MLE. Next, we deal with the problem of testing whether a LCM for multinomial data fits a data set; again, \(\phi \)-divergence measures can be used to generate a family of test statistics generalizing both the classical likelihood ratio test and the chi-squared test statistics. Finally, we treat the problem of choosing the best model out of a sequence of nested LCMs; as before, \(\phi \)-divergence measures can handle the problem and we derive a family of \(\phi \)-divergence test statistics based on them; we study the asymptotic behavior of these test statistics, showing that it is the same as the classical test statistics. A simulation study for small and moderate sample sizes shows that there are some test statistics in the family that can compete with the classical likelihood ratio and the chi-squared test statistics.     

基于区间数据最大似然估计的新方法

In this paper, we study the two-parameter maximum likelihood estimation (MLE)problem for the GE distribution with consideration of interval data. In the presence of interval data, the analytical forms for the restricted MLE of the parameters of GE distribution do not exist. Since interval data is kind of incomplete data, the EM algorithm can be applied to compute the MLEs of the parameters. However the EM algorithm could be less effective.To improve effectiveness, an equivalent lifetime method is employed. The two methods are discussed via simulation studies.     

Double Shrinkage Estimators in the GMANOVA Model

In the GMANOVA model or equivalent growth curve model, shrinkage effects on the MLE (maximum likelihood estimator) are considered under an invariant risk matrix. We first study the fundamental structure of the problem through which we decompose the estimation problem into some conditional problems and then demonstrate some classes of double shrinkage minimax estimators which uniformly dominate the MLE in the matrix risk.     

On the robust estimation in poisson processes with periodic intensities

Under some regularity conditions, it is well known that the maximum likelihood estimator (MLE) is asymptotically normal and efficient. However, if the observation is contaminated, the MLE is not always an appropriate estimator. In this paper, we treat M-estimators and study their asymptotic behavior. By choosing estimation equations, robust M-estimators are presented for phase parameters.     

Asymptotic distributions in the testing and estimation of the missing-data multivariate normal linear patterned mean and correlation matrix

Techniques used by Szatrowski (1979, 1983) to solve the testing and estimation problem for linear patterned covariance are used to obtain results for the linear patterned correlation problem in the presence of missing data. Iterative algorithms are given for finding the maximum-likelihood estimates (MLE). Asymptotic distributions of the MLE and likelihood-ratio statistics (LRS) are obtained using the delta method.     

Estimation of a common parameter for pooled samples from the uniform distributions

Summary The problem to estimate a common parameter for the pooled sample from the uniform distributions is discussed in the presence of nuisance parameters. The maximum likelihood estimator (MLE) and others are compared and it is shown that the MLE based on the pooled sample is not (asymptotically) efficient.     

基于分组数据的回归系数的估计

本文讨论了如何去解决基于分组数据下的回归系数的估计问题.本文所讨论的基于分组数据下的回归模型与经典回归模型的差异在于因变量的观测值为分组数据,即我们只知道它落于事先确定的一组区间中的某一区间,而不知道它的具体值;而经典回归模型的因变量观测值则是一个确定的数值.我们用MLE去估计回归系数,但是此时的MLE无显式解,所以寻找一个合适的迭代算法就成了问题的关键.我们选择利用Bayes计算方法中的EM算法来获得估计量的迭代公式.随机模拟显示了所得估计的有效性.     

Method of Moments Estimators and Multi–step MLE for Poisson Processes

We introduce two types of estimators of the finite–dimensional parameters in the case of observations of inhomogeneous Poisson processes. These are the estimators of the method of moments and Multi–step MLE. It is shown that the estimators of the method of moments are consistent and asymptotically normal and the Multi–step MLE are consistent and asymptotically efficient. The construction of Multi–step MLE–process is done in two steps. First we construct a consistent estimator by the observations on some learning interval and then this estimator is used for construction of One–step and Two–step MLEs. The main advantage of the proposed approach is its computational simplicity.     

On maximum likelihood estimation of a Pareto mixture

In this paper we deal with maximum likelihood estimation (MLE) of the parameters of a Pareto mixture. Standard MLE procedures are difficult to apply in this setup, because the distributions of the observations do not have common support. We study the properties of the estimators under different hypotheses; in particular, we show that, when all the parameters are unknown, the estimators can be found maximizing the profile likelihood function. Then we turn to the computational aspects of the problem, and develop three alternative procedures: an EM-type algorithm, a Simulated Annealing and an algorithm based on Cross-Entropy minimization. The work is motivated by an application in the operational risk measurement field: we fit a Pareto mixture to operational losses recorded by a bank in two different business lines. Under the assumption that each population follows a Pareto distribution, the appropriate model is a mixture of Pareto distributions where all the parameters have to be estimated.