共查询到19条相似文献，搜索用时 156 毫秒
1.
当收益率的协方差矩阵为奇异矩阵时,"均值-方差"模型的最优投资组合问题不宜直接求解.本文结合主成分分析理论、正交变换和凸规划的求解方法,提出求解非负变量条件下协方差矩阵为半正定"均值-方差"模型的有效方法. 相似文献
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王道华 《安徽广播电视大学学报》2006,(3):39-42
讨论了在Markowitz均值-方差模型的基础上引入一种新的投资组合综合模型———风险和效益综合模型,讨论当方差-协方差矩阵正定和半正定时的最优投资组合问题,并利用主成分分析法得到了模型的解析解,从而对于原来的模型作了一定的扩展。 相似文献
3.
以模糊数的截集为切入点给出一种模糊数可能性均值、可能性方差和可能性协方差的新的定义,并将新定义下的可能性均值作为证券组合投资未来收益率的度量,新定义下的可能性方差作为证券组合投资未来风险的度量,构建基于λ-截集的可能性均值-方差组合投资理论模型。并结合中国证券市场中的具体实例,说明了该模型的合理性和适用性。 相似文献
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针对投资组合均值-方差模型,引入了Fast-MCD多变量稳健估计方法,稳健估计模型中股票期望收益和协方差矩阵,减小了离群值对投资组合决策的影响,并结合我国证券市场的特点,对沪市A股市场进行了实证分析,得到了证券投资组合的有效前沿. 相似文献
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序列相关,一般指同一个随机变量逐次值之间的相关。我们这里讨论的是,回归模型Y=Xβ ε中随机扰动项的逐次值之间的相关。在讨论这一问题时,仍然假定随机扰动项的均值为0,且同方差,只是各次扰动项之间的协方差不等于0,即是相互关联的,于是我们考虑模型为 相似文献
6.
沈亦一 《上海工程技术大学教育研究》2014,(3)
在具体应用中,计算样本均值珔X与样本方差S2的期望和方差、Xi-珔X与Xj-珔X协方差以及相关系数是很有必要的,本文给出了相应的计算公式,从而提供了一些简便的计算方法。 相似文献
7.
通过对58Ni(n,2n)反应大量实验数据的收集、分析以及评价,给出了在20Me V以下的实验数据的评价截面和协方差矩阵,为最终给出推荐的58Ni(n,2n)截面数据和高可信度的协方差数据提供了基础。 相似文献
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通过对^58Ni（n,2n）反应大量实验数据的收集、分析以及评价,给出了在20Me V以下的实验数据的评价截面和协方差矩阵,为最终给出推荐的58Ni（n,2n）截面数据和高可信度的协方差数据提供了基础。 相似文献
10.
毕小岩 《新校园(当代教育研究)》2009,(7)
高中新课程人教A版选修2-3第二章<随机变量及其分布>中,课本介绍了三种分布列--两点分布、二项分布、超几何分布,前两者的均值与方差,课本给出了明确的公式,但是超几何分布的均值与方差课本并未给出,笔者现给出其中学数学的解答方法. 相似文献
11.
方差分量模型的随机效应的协方差为单位阵时<线性模型引论>已进行研究.把随机效应的协方差推广为正定阵进行研究.用最小范数二次无偏估计法给出方差分量的估计. 相似文献
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We present a LISREL model for the estimation of the repeated measures analysis of variance (ANOVA) with a patterned covariance matrix. We demonstrate this model for a 5 x 2 (Time x Group) ANOVA in which the data are assumed to be serially correlated. The residuals are assumed to have been generated according to an AR(1) process. An appropriate covariance structure is selected, tested, and compared with alternative patterns. Similarities between the LISREL model and SAS PROC MIXED are discussed. 相似文献
13.
Mariola Moeyaert Maaike Ugille John M. Ferron S. Natasha Beretvas Wim Van den Noortgate 《Journal of Experimental Education》2016,84(3):473-509
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest lies in the between-study variance estimate, including at least 30 studies is warranted. Modeling covariance does not result in less biased between-study variance estimates as the between-study covariance estimate is biased. When the research interest lies in the between-case covariance, the model including covariance results in unbiased between-case variance estimates. The three-level model appears to be less appropriate for estimating between-study variance if fewer than 30 studies are included. 相似文献
14.
讨论了一类基于T-S模糊模型的非线性系统,根据满意控制思想提出一种条件约束下的状态反馈控制设计方法。即在H∞控制基础上,加入圆形极点指标和状态协方差指标约束。进而研究了圆形极点约束和状态协方差约束下,系统被控输出对扰动输入的H∞抑制界优化问题,并且将H∞优化、圆形极点和状态协方差指标约束的状态反馈控制器设计归结为求一组线性矩阵不等式(LMI)的可行解问题,通过求解LMI得到满足要求的控制器参数。仿真结果表明该方法可行有效。 相似文献
15.
非线性模型中无信息方差和协方差分量Bayes估计 总被引：1，自引：1，他引：0
闫同新 《福建工程学院学报》2009,7(4)
采用Bayes方法从无先验信息出发,得到了非线性模型中方差和协方差分量的估计(包含相关系数),最后通过实例解算,结果表明:非线性模型中方差和协方差分量的估计,与ρ的理论值-0.5偏差不大,当没有先验信息时,该方法是可行的. 相似文献
16.
Ehri Ryu 《Structural equation modeling》2013,20(4):617-630
This article examined the role of centering in estimating interaction effects in multilevel structural equation models. Interactions are typically represented by product term of 2 variables that are hypothesized to interact. In multilevel structural equation modeling (MSEM), the product term involving Level 1 variables is decomposed into within-cluster and between-cluster random components. The choice of centering affects the decomposition of the product term, and therefore affects the sample variance and covariance associated with the product term used in the maximum likelihood fitting function. The simulation study showed that for an interaction between a Level 1 variable and a Level 2 variable, the product term of uncentered variables or the product term of grand mean centered variables produced unbiased estimates in both Level 1 and Level 2 models. The product term of cluster mean centered variables produced biased estimates in the Level 1 model. For an interaction between 2 Level 1 variables, the product term of cluster mean centered variables produced unbiased estimates in the Level 1 model, whereas the product term of grand mean centered variables produced unbiased estimates for the Level 1 model. Recommendations for researchers who wish to estimate interactions in MSEM are provided. 相似文献
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Growth curve modeling provides a general framework for analyzing longitudinal data from social, behavioral, and educational sciences. Bayesian methods have been used to estimate growth curve models, in which priors need to be specified for unknown parameters. For the covariance parameter matrix, the inverse Wishart prior is most commonly used due to its proper and conjugate properties. However, many researchers have pointed out that the inverse Wishart prior might not work as expected. The purpose of this study is to investigate the influence of the inverse Wishart prior and compare it with a class of separation-strategy priors on the parameter estimates of growth curve models. In this article, we illustrate the use of different types of priors with 2 real data analyses, and then conduct simulation studies to evaluate and compare these priors in estimating both linear and nonlinear growth curve models. For the linear model, the simulation study shows that both the inverse Wishart and the separation-strategy priors work well for the fixed effects parameters. For the Level 1 residual variance estimate, the separation-strategy prior performs better than the inverse Wishart prior. For the covariance matrix, the results are mixed. Overall, the inverse Wishart prior is suggested if the population correlation coefficient and at least 1 of the 2 marginal variances are large. Otherwise, the separation-strategy prior is preferred. For the nonlinear growth curve model, the separation-strategy priors work better than the inverse Wishart prior. 相似文献
18.
In the application of the Satorra–Bentler scaling correction, the choices of normal-theory weight matrices (i.e., the model-predicted vs. the sample covariance matrix) in the calculation of the correction remains unclear. Different software programs use different matrices by default. This simulation study investigates the discrepancies due to the weight matrices in the robust chi-square statistics, standard errors, and chi-square-based model fit indexes. This study varies the sample sizes at 100, 200, 500, and 1,000; kurtoses at 0, 7, and 21; and degrees of model misspecification, measured by the population root mean square error of approximation (RMSEA), at 0, .03, .05, .08, .10, and .15. The results favor the use of the model-predicted covariance matrix because it results in less false rejection rates under the correctly specified model, as well as more accurate standard errors across all conditions. For the sample-corrected robust RMSEA, comparative fit index (CFI) and Tucker–Lewis index (TLI), 2 matrices result in negligible differences. 相似文献
19.
Models of change typically assume longitudinal measurement invariance. Key constructs are often measured by ordered-categorical indicators (e.g., Likert scale items). If tests based on such indicators do not support longitudinal measurement invariance, it would be useful to gauge the practical significance of the detected non-invariance. The authors focus on the commonly used second-order latent growth curve model, proposing a sensitivity analysis that compares the growth parameter estimates from a model assuming the highest achieved level of measurement invariance to those from a model assuming a higher, incorrect level of measurement invariance as a measure of practical significance. A simulation study investigated the practical significance of non-invariance in different locations (loadings, thresholds, uniquenesses) in second-order latent linear growth models. The mean linear slope was affected by non-invariance in the loadings and thresholds, the intercept variance was affected by non-invariance in the uniquenesses, and the linear slope variance and intercept–slope covariance were affected by non-invariance in all three locations. 相似文献