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measure of goodness
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  “measure of goodness”译为未确定词的双语例句
     The formulas of error estimates and statistical measure of goodness-of-fit are given. On the basis of these, a program SIMFIT is written in NDP-FORTRAN 386 lan-guage. Different fitting methods and fitting functions can be used in this p rogram.
     介绍了计算弛豫时间的数据拟合原理、拟合误差估计及拟合优度的统计度量,在此基础上,用NDP一FORTRAN386语言自编了拟合程序SIMFIT,该程序能提供不同的拟合方法和多种拟合函数供使用,并能显示拟合曲线和拟合误差,特别适用多指数弛豫的数据拟合。
短句来源
     To form a basis for model stractur discrimination, a measure of goodness related to the autocorrelation of residuals and to the accuracy of the parameter estimates is chosen.
     判定模型结构优良度的依据是残差的相关性以及参数估计的精度。
短句来源
     Using multilevel CFA model obtained a more meaningful measure of goodness of fit.
     进一步对模型进行修正后,模型拟合较好。
短句来源
  相似匹配句对
     Knowledge Measure
     知识测度
短句来源
     Extension of Measure
     测度的扩张(英文)
短句来源
     The Measure of Fuzzines
     Fuzzy性度量
短句来源
     S- measure and its Extension
     S─测度及其扩张
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     Technology and Goodness
     技术与善
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  measure of goodness
A generalized Euclidean distance metric which indexes the average distance between an observed payoff vector and the entire set of predicted payoff vectors (Bonacich, 1979) was used as the measure of goodness-of-fit.
      
The paper defines a path exposure metric as a measure of goodness of deployment.
      
Interpreting entropy as a prior probability suggests a universal but "purely empirical" measure of "goodness of fit." This allows statistical techniques to be used in situations where the correct theory- and not just its parameters-is still unknown.
      
It outlines testing techniques, test coverage, granularity of test results and 'measure of goodness' of the tested product.
      
In this paper, we describe the Composite Approach, the IFM, the MPP, the Measure of Goodness and work out several examples in detail.
      
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In this paper, a new method for structure discriminaton of models for stationary time series is proposed. To form a basis for model stractur discrimination, a measure of goodness related to the autocorrelation of residuals and to the accuracy of the parameter estimates is chosen. Using the concept of information entropy, a measure function of the autocorrelation of residuals is established. In order to measure the parameter accuracy, a suitable chosen scalar function of Fisher's information...

In this paper, a new method for structure discriminaton of models for stationary time series is proposed. To form a basis for model stractur discrimination, a measure of goodness related to the autocorrelation of residuals and to the accuracy of the parameter estimates is chosen. Using the concept of information entropy, a measure function of the autocorrelation of residuals is established. In order to measure the parameter accuracy, a suitable chosen scalar function of Fisher's information matrix is introduced. and a numerical method for calculating the information matrix is derived. Finally, several digital simulation examples to illustrate the effectiveness and the practicality of the method proposed are given.

本文提出一种平稳时间序列模型结构判定的新方法。判定模型结构优良度的依据是残差的相关性以及参数估计的精度。利用信息熵的概念,文中建立了衡量残差相关性的度量函数。为了度量参数的精度,引入费歇尔信息矩阵的纯量函数,并导出了信息矩阵计算的数值方法,最后,列举了一些数字仿真的例子,以说明本文所提出的方法的有效性和实用性。

he data fit theory of relaxation time measurement is intriduced in this paper.The formulas of error estimates and statistical measure of goodness-of-fit are given.On the basis of these, a program SIMFIT is written in NDP-FORTRAN 386 lan-guage. Different fitting methods and fitting functions can be used in this p rogram.The fit curve and fit error can be displayed on the screen.

介绍了计算弛豫时间的数据拟合原理、拟合误差估计及拟合优度的统计度量,在此基础上,用NDP一FORTRAN386语言自编了拟合程序SIMFIT,该程序能提供不同的拟合方法和多种拟合函数供使用,并能显示拟合曲线和拟合误差,特别适用多指数弛豫的数据拟合。

Objective To study how to evaluate construct validity using multilevel CFA model for hierarchical structure data.Methods Occupational stress data were analyzed and using LISREL to estimate parameter.RMSEA,GFI,SRMR were used as fitting goodness indicator.Results Parameter may be underestimated using typical CFA model for hierarchical structure questionnaire data.Using multilevel CFA model obtained a more meaningful measure of goodness of fit.Conclusion Multilevel CFA model make the factor assumption and...

Objective To study how to evaluate construct validity using multilevel CFA model for hierarchical structure data.Methods Occupational stress data were analyzed and using LISREL to estimate parameter.RMSEA,GFI,SRMR were used as fitting goodness indicator.Results Parameter may be underestimated using typical CFA model for hierarchical structure questionnaire data.Using multilevel CFA model obtained a more meaningful measure of goodness of fit.Conclusion Multilevel CFA model make the factor assumption and validity estimate more reasonable for hierarchical structure data.

目的探讨如何利用多水平CFA模型对系统结构数据进行构念(结构)效度的评价。方法结合职业紧张研究数据,利用LISREL软件实现参数估计,以RMSEA、GFI、SRMR作为拟合优度检验的评价指标。结果对具有系统结构特征的问卷调查数据在评价构念效度时,采用单水平模型拟合,参数估计有偏低倾向,经多水平CFA模型拟合优度检验结果有所改进。进一步对模型进行修正后,模型拟合较好。结论估计具有系统结构特征的问卷调查数据的效度,采用多水平CFA模型更趋合理。

 
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