共查询到19条相似文献,搜索用时 74 毫秒
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利用数学分析中的Lagrange乘数法证明了一些名的不等式,阐述了其在不等式证明中的使用方法。 相似文献
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在当今西方经济学至少是所谓的"高级"西方经济理论中,数学已经成为阐述理论内容的基本要素或者说是通用语言.线性回归模型是最常用的经济计量模型,用于研究风险、保险、资产组合等经济问题,也可以用作经济预测.本文研究了线性回归模型中的参数估计问题,运用最小二乘法进行参数估计的数学分析基础.最小二乘法只是使用了函数的稳定点而没有运用数学分析的极值理论里的第一充分条件和第二充分条件,本文讨论最小二乘法的估计参数方法和数学分析的极值理论的方法是等价的. 相似文献
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余长青 《黔南民族师范学院学报》2005,25(6):53-56
只有实验数据在未知函数关系或已知函数关系较为复杂的情况下,怎样用一个简单的解析式来较为准确地描述它。本文从最小二乘原理开始,介绍了求解的过程,并通过实例说明了最小二乘法的应用。 相似文献
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马兴科 《周口师范学院学报》1995,(2)
在科学实验和统计研究中,往往需要从一组测得的数据(x_i,y_i)(i=1.2,…,m)中去求自变量x与因变量y之间的函数关系y=f(x),当然,一般求得的只是y=f(x)的一个近似关系式。 最小二乘法又称曲线拟合。所谓“拟合”,即不要求所作的曲线完全通过所有的数据点,只要求所得的近似曲线能反映数据的基本趋势,它的实质是离散情况下的最小平方逼近。它 相似文献
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应用最小二乘法对光电效应法测普朗克常数实验中的数据进行处理,提高准确度,使得到相对比较准确的普朗克常数。 相似文献
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用最小二乘法来处理普通物理实验数据和拟合曲线,此项工作涉及到大量的计算。若以手工计算会更加烦琐。但如果借助计算机编程计算,则处理的速度就会大大地加快,使得问题简单化。 相似文献
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首先将大量的经验数据,使用MATLAB工具,快速找到线性较好的测量区间,并采用最小二乘原理,拟合出测量区间的曲线方程,然后通过对电磁感应测量电路的智能微控制单元进行编程调整,从而使该测量电路具有高精度和自适应调整功能. 相似文献
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基于模糊互补判断矩阵的一致性定义,利用对数最小二乘法原理,提出了一种新的排序方法——模糊对数最小二乘法.研究了此排序方法的一些优良性质,并将其应用到液晶显示器投资中的指标评价中。 相似文献
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Deborah L. Bandalos 《Structural equation modeling》2013,20(1):102-116
Robust maximum likelihood (ML) and categorical diagonally weighted least squares (cat-DWLS) estimation have both been proposed for use with categorized and nonnormally distributed data. This study compares results from the 2 methods in terms of parameter estimate and standard error bias, power, and Type I error control, with unadjusted ML and WLS estimation methods included for purposes of comparison. Conditions manipulated include model misspecification, level of asymmetry, level and categorization, sample size, and type and size of the model. Results indicate that cat-DWLS estimation method results in the least parameter estimate and standard error bias under the majority of conditions studied. Cat-DWLS parameter estimates and standard errors were generally the least affected by model misspecification of the estimation methods studied. Robust ML also performed well, yielding relatively unbiased parameter estimates and standard errors. However, both cat-DWLS and robust ML resulted in low power under conditions of high data asymmetry, small sample sizes, and mild model misspecification. For more optimal conditions, power for these estimators was adequate. 相似文献
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《Structural equation modeling》2013,20(2):186-203
This simulation study compared maximum likelihood (ML) estimation with weighted least squares means and variance adjusted (WLSMV) estimation. The study was based on confirmatory factor analyses with 1, 2, 4, and 8 factors, based on 250, 500, 750, and 1,000 cases, and on 5, 10, 20, and 40 variables with 2, 3, 4, 5, and 6 categories. There was no model misspecification. The most important results were that with 2 and 3 categories the rejection rates of the WLSMV chi-square test corresponded much more to the expected rejection rates according to an alpha level of. 05 than the rejection rates of the ML chi-square test. The magnitude of the loadings was more precisely estimated by means of WLSMV when the variables had only 2 or 3 categories. The sample size for WLSMV estimation needed not to be larger than the sample size for ML estimation. 相似文献
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This study compared diagonal weighted least squares robust estimation techniques available in 2 popular statistical programs: diagonal weighted least squares (DWLS; LISREL version 8.80) and weighted least squares–mean (WLSM) and weighted least squares—mean and variance adjusted (WLSMV; Mplus version 6.11). A 20-item confirmatory factor analysis was estimated using item-level ordered categorical data. Three different nonnormality conditions were applied to 2- to 7-category data with sample sizes of 200, 400, and 800. Convergence problems were seen with nonnormal data when DWLS was used with few categories. Both DWLS and WLSMV produced accurate parameter estimates; however, bias in standard errors of parameter estimates was extreme for select conditions when nonnormal data were present. The robust estimators generally reported acceptable model–data fit, unless few categories were used with nonnormal data at smaller sample sizes; WLSMV yielded better fit than WLSM for most indices. 相似文献