共查询到18条相似文献，搜索用时 125 毫秒
1.
李晓康 《河北职业技术学院学报》2011,(4)
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。 相似文献
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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. 相似文献
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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. 相似文献
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5.
徐晨 《中国校外教育(理论)》2014,(27):118
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。 相似文献
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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。 相似文献
7.
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。 相似文献
8.
非参数回归模型的模型检验 总被引：1，自引：1，他引：0
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. 相似文献
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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。 相似文献
10.
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。 相似文献
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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. 相似文献
12.
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. 相似文献
13.
Shaun McQuitty 《Structural equation modeling》2013,20(3):244-252
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. 相似文献
14.
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. 相似文献
15.
Alberto Maydeu-Olivares 《Structural equation modeling》2017,24(3):383-394
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. 相似文献
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We introduce and evaluate a new class of approximations to common test statistics in structural equation modeling. Such test statistics asymptotically follow the distribution of a weighted sum of i.i.d. chi-square variates, where the weights are eigenvalues of a certain matrix. The proposed eigenvalue block averaging (EBA) method involves creating blocks of these eigenvalues and replacing them within each block with the block average. The Satorra–Bentler scaling procedure is a special case of this framework, using one single block. The proposed procedure applies also to difference testing among nested models. We investigate the EBA procedure both theoretically in the asymptotic case, and with simulation studies for the finite-sample case, under both maximum likelihood and diagonally weighted least squares estimation. Comparison is made with 3 established approximations: Satorra–Bentler, the scaled and shifted, and the scaled F tests. 相似文献
17.
The accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) × 3 (exogenous latent mean difference) × 3 (endogenous latent mean difference) × 3 (correlation between factors) × 3 (mixture proportions) factorial design. In addition, the efficacy of several likelihood-based statistics (Akaike's Information Criterion [AIC], Bayesian Information Ctriterion [BIC], the sample-size adjusted BIC [ssBIC], the consistent AIC [CAIC], the Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test [aVLMR]), classification-based statistics (CLC [classification likelihood information criterion], ICL-BIC [integrated classification likelihood], normalized entropy criterion [NEC], entropy), and distributional statistics (multivariate skew and kurtosis test) were examined to determine which statistics best recover the correct number of components. Results indicate that the structural parameters were recovered, but the model fit statistics were not exceedingly accurate. The ssBIC statistic was the most accurate statistic, and the CLC, ICL-BIC, and aVLMR showed limited utility. However, none of these statistics were accurate for small samples (n = 500). 相似文献
18.
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。 相似文献