二项分布中p~2的矩估计与极大似然估计

基于二项分布B(n,p)总体,给出了未知参数p2的矩估计和极大似然估计,并讨论了估计量的无偏性。     

极大似然估计的教学设计之我见

本文按照将学习的主动权还给学生的指导思想,提出极大似然估计这节课的课堂教学过程设计。     

极大似然估计的朴素理解

极大似然估计是一种广泛应用的统计方法,本文旨在以一种大众化的语言使读者至少必须搞清楚几个问题:估计什么?需要什么前提或假设?如何估计?估计的准确度如何?     

极大似然估计及其应用

讨论了极大似然估计原理及求法,说明了极大似然估计在不同分布和参数空间的应用,阐述了生命表中在单风险非完整样本数据环境中表格生存模型的极大似然估计方法．     

极大似然法的基本思想及其应用

本叙述了建立极大似然法的基本思想，以及如何利用这种方法进行参数估计。     

均匀分布场合下参数的极大似然估计

针对不同区间上的均匀分布,应用次序统计量,给出了未知参数的极大似然估计,并讨论了估计量的无偏性。     

等价限制线性模型中极大似然估计的稳健性

稳健性分析是判断估计值与真实值之间差异是否重要的一种方法。限制线性模型下的极大似然估计的稳健性是当前大家比较感兴趣的一个问题。笔者在前人的基础上,给出限制线性模型中极大似然估计对随机误差协方差矩阵的稳健性统计量,并对其进行分析,得出限制线性模型中极大似然估计对随机误差协方差矩阵不敏感。     

一类单参数均匀分布上参数极大似然估计及优良性的讨论

文中对U={θ-1/2,θ＋1/2}上参数θ常用的三个极大似然估计及修正后的极大似然估计,从均方误差角度进行比较,得出了较优的估计,并证明其相合性及UMVUE．     

Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models

Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects modeling (LMM) such as cross-sectional multilevel modeling and latent growth modeling. It is well known that LMM can be formulated as structural equation models. However, one main difference between the implementations in SEM and LMM is that maximum likelihood (ML) estimation is usually used in SEM, whereas restricted (or residual) maximum likelihood (REML) estimation is the default method in most LMM packages. This article shows how REML estimation can be implemented in SEM. Two empirical examples on latent growth model and meta-analysis are used to illustrate the procedures implemented in OpenMx. Issues related to implementing REML in SEM are discussed.     

泊松分布中参数的经典估计与Bayes估计的相等关系

文中证明了泊松分布中未知参数的矩估计和最大似然估计,一定存在一个先验分布,使其贝叶斯估计就是该经典估计的结论.     

广义线性模型极大似然估计弱相合性的数值模拟

广义线性模型是经典线性模型的自然推广,它是一类应用广泛的统计模型.选择Probit模型,利用matlab编程,通过数值模拟的方法验证了广义线性模型的极大似然估计的弱相合性.     

Maximum Likelihood Dynamic Factor Modeling for Arbitrary N and T Using SEM

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary T and N by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time series analysis (T large and N = 1) and conventional SEM (N large and T = 1 or small) by integrating both approaches. The resulting combined model offers a variety of new modeling options including a direct test of the ergodicity hypothesis, according to which the factorial structure of an individual observed at many time points is identical to the factorial structure of a group of individuals observed at a single point in time. Third, we illustrate the flexibility of SEM time series modeling by extending the approach to account for complex error structures. We end with a discussion of current limitations and future applications of SEM-based time series modeling for arbitrary T and N.     

对数正态分布的几个性质及其参数估计

给出对数正态分布的几个性质,分别利用矩估计法和最大似然估计法求出对数正态分布参数的点估计,并讨论其参数的区间估计。     

对数正态分布的几个性质及其参数估计

给出对数正态分布的几个性质,分别利用矩估计法和最大似然估计法求出对数正态分布参数的点估计,并讨论其参数的区间估计。     

矩估计法的理论注释

参数的矩估计法中,用样本原点矩作为总体原点矩的估计量是其中心思想。本文将给出这一思想的理论注释。     

对简支梁绝对最大弯矩的分析

通过理论推导，确定了简支梁绝对最大弯矩与跨中截面最大弯矩差值的上限，从而对实际设计工作提出了有益的建议．