基于似然比统计量的正态总体的检验

假设检验问题的关键是在一定的统计思想之下构造相关的统计量。极大似然思想是统计和实际应用中的一种重要思想。基于极大似然原理,构造似然比统计量,讨论正态总体的检验问题,其结果与传统的U检验、T检验、χ2检验一致。     

基于最大L_q似然估计的广义非线性模型的统计诊断(英文)

为了检测数据是否符合给定的模型,需要对数据进行统计诊断.研究了基于最大Lq似然估计的广义非线性模型的统计诊断问题.利用3个统计诊断量来检验数据中是否都存在异常点.模拟结果显示,当样本容量较小时,使用最大Lq似然估计方法得到的诊断统计量的结果要比使用极大似然估计(MLE)方法得到的结果大;随着样本容量的增加,它们之间的区别逐渐减小.因此,使用最大Lq似然估计方法比用MLE方法更容易找到数据中的异常点.     

极大似然估计的性质探讨

文章首先介绍了极大似然估计,然后综述了极大似然估计的优良性质,同时探讨了在应用极大似然估计时所应注意的问题。     

极大似然估计量的几种求法

一般概率统计教材主要介绍用微分法(见文中方法一)求参数的极大似然估计量,而某些总体参数的极大似然估计量用上述方法很难甚至不能求出,本文以例题的形式总结了参数极大似然估计量的几种求法.一、微分法这是一种用导数求似然函数最大值点的方法.各种教材对此法均有介绍,但应用实例却很少,现举一实际应用的例子.     

大学数理统计教学中有关极大似然估计方法的课堂讲授

极大似然估计在参数的点估计方法中是一个重要的估计方法,并且其估计值有很多优良的统计性质。在教学中,由于此方法计算较为复杂,学生学习起来较为困难。主要介绍了极大似然估计的容易理解的课堂讲授方法。     

两个0-1总体具有部分缺失数据时的估计和检验

文章主要讨论了两个0-1总体均具有部分缺失数据时的参数的极大似然估计,并证明了估计的强相合性和渐进正态性,给出了大样本场合下的似然比检验统计量的极限分布。     

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。     

非参数回归模型的模型检验

在此考虑非参数回归模型的模型检验问题.基于Plug-in经验似然方法,构造经验似然比检验统计量.证明其满足Wilks’现象,而得到了一定显著性水平的拒绝域.最后通过数据模拟,讨论了其检验功效.     

相协样本下密度函数在有限个点处的联合经验似然置信域

研究相协样本下密度函数在有限个不同点上的联合经验似然置信域的构造,证明了相协样本下密度函数在r个不同点上的联合经验似然比统计量的极限分布为χr2,由此结果构造密度函数在r个不同点上的联合经验似然置信域。     

右截尾数据处理方法简析

电子元器件可靠性寿命分析中,右截尾类型的数据居多,针对此类数据的分析方法也很多。其中,线性回归和极大似然估计应用较广。在此,运用实例对这两种方法进行了对比分析,指出了使用线性回归法进行参数估计的缺点,说明极大似然估计法是比线性回归法更优的统计模型。     

Evaluation of a New Mean Scaled and Moment Adjusted Test Statistic for SEM

Recently a new mean scaled and skewness adjusted test statistic was developed for evaluating structural equation models in small samples and with potentially nonnormal data, but this statistic has received only limited evaluation. The performance of this statistic is compared to normal theory maximum likelihood and 2 well-known robust test statistics. A modification to the Satorra–Bentler scaled statistic is developed for the condition that sample size is smaller than degrees of freedom. The behavior of the 4 test statistics is evaluated with a Monte Carlo confirmatory factor analysis study that varies 7 sample sizes and 3 distributional conditions obtained using Headrick's fifth-order transformation to nonnormality. The new statistic performs badly in most conditions except under the normal distribution. The goodness-of-fit χ² test based on maximum-likelihood estimation performed well under normal distributions as well as under a condition of asymptotic robustness. The Satorra–Bentler scaled test statistic performed best overall, whereas the mean scaled and variance adjusted test statistic outperformed the others at small and moderate sample sizes under certain distributional conditions.     

Evaluation of Test Statistics for Robust Structural Equation Modeling With Nonnormal Missing Data

A 2-stage robust procedure as well as an R package, rsem, were recently developed for structural equation modeling with nonnormal missing data by Yuan and Zhang (2012). Several test statistics that have been used for complete data analysis are employed to evaluate model fit in the 2-stage robust method. However, properties of these statistics under robust procedures for incomplete nonnormal data analysis have never been studied. This study aims to systematically evaluate and compare 5 test statistics, including a test statistic derived from normal-distribution-based maximum likelihood, a rescaled chi-square statistic, an adjusted chi-square statistic, a corrected residual-based asymptotical distribution-free chi-square statistic, and a residual-based F statistic. These statistics are evaluated under a linear growth curve model by varying 8 factors: population distribution, missing data mechanism, missing data rate, sample size, number of measurement occasions, covariance between the latent intercept and slope, variance of measurement errors, and downweighting rate of the 2-stage robust method. The performance of the test statistics varies and the one derived from the 2-stage normal-distribution-based maximum likelihood performs much worse than the other four. Application of the 2-stage robust method and of the test statistics is illustrated through growth curve analysis of mathematical ability development, using data on the Peabody Individual Achievement Test mathematics assessment from the National Longitudinal Survey of Youth 1997 Cohort.     

A Regularized GLS for Structural Equation Modeling

Ill conditioning of covariance and weight matrices used in structural equation modeling (SEM) is a possible source of inadequate performance of SEM statistics in nonasymptotic samples. A maximum a posteriori (MAP) covariance matrix is proposed for weight matrix regularization in normal theory generalized least squares (GLS) estimation. Maximum likelihood (ML), GLS, and regularized GLS test statistics (RGLS and rGLS) are studied by simulation in a 15-variable, 3-factor model with 15 levels of sample size varying from 60 to 100,000. A key result showed that in terms of nominal rejection rates, RGLS outperformed ML at all sample sizes below 500, and GLS at most sample sizes below 500. In larger samples, their performance was equivalent. The second regularization methodology (rGLS) performed well asymptotically, but poorly in small samples. Regularization in SEM deserves further study.     

Maximum Likelihood Estimation: for the Multinomial Distribution

In a first course in mathematical statistics or categorical data analysis, the maximum likelihood estimators of the parameters of the multinomial distribution are often “derived” using calculus. However, the important step of verifying that the resulting estimators indeed maximize the likelihood is omitted or done incorrectly. In this paper, two simple methods of obtaining the maximum likelihood estimates are presented.     

Effects of employing ridge regression in structural equation models

LISREL 8 invokes a ridge option when maximum likelihood or generalized least squares are used to estimate a structural equation model with a nonpositive definite covariance or correlation matrix. The implications of the ridge option for model fit statistics, parameter estimates, and standard errors are explored through the use of 2 examples. The results indicate that maximum likelihood estimates are quite stable with the ridge option, though fit statistics and standard errors vary considerably and therefore cannot be trusted. As a result of these findings, the application of the ridge method to structural equation models is not recommended.     

Psychometric Properties of IRT Proficiency Estimates

Psychometric properties of item response theory proficiency estimates are considered in this paper. Proficiency estimators based on summed scores and pattern scores include non-Bayes maximum likelihood and test characteristic curve estimators and Bayesian estimators. The psychometric properties investigated include reliability, conditional standard errors of measurement, and score distributions. Four real-data examples include (a) effects of choice of estimator on score distributions and percent proficient, (b) effects of the prior distribution on score distributions and percent proficient, (c) effects of test length on score distributions and percent proficient, and (d) effects of proficiency estimator on growth-related statistics for a vertical scale. The examples illustrate that the choice of estimator influences score distributions and the assignment of examinee to proficiency levels. In particular, for the examples studied, the choice of Bayes versus non-Bayes estimators had a more serious practical effect than the choice of summed versus pattern scoring.     

A Scaled F Distribution as an Approximation to the Distribution of Test Statistics in Covariance Structure Analysis

When the assumption of multivariate normality is violated or when a discrepancy function other than (normal theory) maximum likelihood is used in structural equation models, the null distribution of the test statistic may not be χ² distributed. Most existing methods to approximate this distribution only match up to 2 moments. In this article, we propose 2 additional approximation methods: a scaled F distribution that matches 3 moments simultaneously and a direct Monte Carlo–based weighted sum of i.i.d. χ² variates. We also conduct comprehensive simulation studies to compare the new and existing methods for both maximum likelihood and nonmaximum likelihood discrepancy functions and to separately evaluate the effect of sampling uncertainty in the estimated weights of the weighted sum on the performance of the approximation methods.     

Maximum Likelihood Estimation of Structural Equation Models for Continuous Data: Standard Errors and Goodness of Fit

Classical accounts of maximum likelihood (ML) estimation of structural equation models for continuous outcomes involve normality assumptions: standard errors (SEs) are obtained using the expected information matrix and the goodness of fit of the model is tested using the likelihood ratio (LR) statistic. Satorra and Bentler (1994) introduced SEs and mean adjustments or mean and variance adjustments to the LR statistic (involving also the expected information matrix) that are robust to nonnormality. However, in recent years, SEs obtained using the observed information matrix and alternative test statistics have become available. We investigate what choice of SE and test statistic yields better results using an extensive simulation study. We found that robust SEs computed using the expected information matrix coupled with a mean- and variance-adjusted LR test statistic (i.e., MLMV) is the optimal choice, even with normally distributed data, as it yielded the best combination of accurate SEs and Type I errors.