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样条平滑中交叉验证平滑参数的影响诊断AI 解读

This article addresses the problem of influence in estimating the smoothing parameter when fitting a univariate smoothing spline. Diagnostics are presented that can identify observations that locally influence the choice of smoothing parameter by generalized cross-validation, either through their case weights or observed responses. Generalized cross-validation and the diagnostic methods are given a frequency interpretation and illustrated with examples.

广义线性模型回归系数置信区域的影响诊断AI 解读

Influence on confidence regions has received less attention than the problem of influence on regression coefficients in generalized linear models. Diagnostics are given for examining influence on confidence intervals or the confidence region for all of the regression coefficients. These diagnostics permit one to check whether the standard errors of the coefficients are sensitive to the exact specification of case weights, or the observed values of the explanatory variables…

广义线性模型回归系数置信区域的影响诊断AI 解读

Influence on confidence regions has received less attention than the problem of influence on regression coefficients in generalized linear models. Diagnostics are given for examining influence on confidence intervals or the confidence region for all of the regression coefficients. These diagnostics permit one to check whether the standard errors of the coefficients are sensitive to the exact specification of case weights, or the observed values of the explanatory variables…

广义线性模型回归系数的影响诊断AI 解读

Suggested diagnostics for influence on the estimated regression coefficients in a generalized linear model have generally approximated the effect of deleting a single case. We apply the local influence method of Cook (1986) to assess the effect of small perturbations of the data, including the vector of responses, case weights, explanatory variables, and the components of one case. The resulting diagnostics allow one to check for different kinds of influence, and may give…

线性回归中的敏感性分析AI 解读

The Prediction Matrix Role of Variables in a Regression Equation Effects of an Observation on a Regression Equation Assessing the Influence of Multiple Observations Joint Impact of a Variable and an Observation Assessing the Effect of Errors of Measurements A Study of Model Sensitivity by the Generalized Linear Model Approach Computational Considerations. Appendix: Summary of Vector and Matrix Norms, Proofs of Three Theorems.

广义线性模型回归系数的影响诊断AI 解读

Suggested diagnostics for influence on the estimated regression coefficients in a generalized linear model have generally approximated the effect of deleting a single case. We apply the local influence method of Cook (1986) to assess the effect of small perturbations of the data, including the vector of responses, case weights, explanatory variables, and the components of one case. The resulting diagnostics allow one to check for different kinds of influence, and may give…

重新评价厂房与设备投资预期的预测能力AI 解读

This article suggests that anticipations alone do a better job of forecasting business plant and equipment expenditures than fitted functions which include only non-anticipatory data. Anticipations can also make a significant contribution in the presence of such data. Since previously published studies have come to different conclusions, the focus of this article is an evaluation of the methodology and results of those studies.

厂房与设备投资预期预测能力的重新评估AI 解读

This article suggests that anticipations alone do a better job of forecasting business plant and equipment expenditures than fitted functions which include only non-anticipatory data. Anticipations can also make a significant contribution in the presence of such data. Since previously published studies have come to different conclusions, the focus of this article is an evaluation of the methodology and results of those studies.