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首页> 外文期刊>Journal of applied statistics >Graphical Solutions for Structural Regression Assist Errors-in-Variables Modelling
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Graphical Solutions for Structural Regression Assist Errors-in-Variables Modelling

机译：结构回归辅助变量误差建模的图形解决方案

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Structural regression attempts to reveal an underlying relationship by compensating for errors in the variables. Ordinary least-squares regression has an entirely different purpose and provides a relationship between error-included variables. Structural model solutions, also known as the errors-in-variables and measurement-error solutions, use various inputs such as the error-variance ratio and x-error variance. This paper proposes that more accurate structural line gradient (coefficient) solutions will result from using the several solutions together as a system of equations. The known data scatter, as measured by the correlation coefficient, should always be used in choosing legitimate combinations of x- and y-error terms. However, this is difficult using equations. Chart solutions are presented to assist users to understand the structural regression process, to observe the correlation coefficient constraint, to assess the impact of their error estimates and, therefore, to provide better quality estimates of the structural regression gradient.
机译：结构回归试图通过补偿变量中的误差来揭示潜在的关系。普通最小二乘回归的目的完全不同，并且提供了包含错误的变量之间的关系。结构模型解决方案（也称为变量误差和测量误差解决方案）使用各种输入，例如误差方差比和x误差方差。本文提出，通过将多个解一起用作方程组，可以得到更准确的结构线梯度（系数）解。通过相关系数测得的已知数据散布应始终用于选择x和y误差项的合法组合。但是，使用等式很难。提出了图表解决方案，以帮助用户了解结构回归过程，观察相关系数约束，评估其误差估计的影响，从而为结构回归梯度提供更好的质量估计。

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