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误差具有序列相关的交叉滞后面板模型AI 解读

Cross-lagged panel studies are studies in which two or more variables are measured for a large number of subjects at each of several points in time. The variables divide naturally into two sets, and the purpose of the analysis is to estimate and test the cross-effects between the two sets. One approach to this analysis is to treat the cross-effects as parameters in regression equations. This study contributes to this approach by extending the regression model to a…

标准化回归系数的估计AI 解读

Often in reporting the results of a regression analysis, researchers, particularly in the social sciences, choose to standardize the estimators of the regression coefficients into what are called “beta coefficients.” Most studies in which beta coefficients are reported involve linear models which contain stochastic predictor variables. However, in dealing with regression models in which thep redictor variables are nonstochastic, standardized regression coefficients can be…

标准化回归系数的估计AI 解读

Often in reporting the results of a regression analysis, researchers, particularly in the social sciences, choose to standardize the estimators of the regression coefficients into what are called “beta coefficients.” Most studies in which beta coefficients are reported involve linear models which contain stochastic predictor variables. However, in dealing with regression models in which thep redictor variables are nonstochastic, standardized regression coefficients can be…

利用初始估计改进线性模型系数估计AI 解读

A procedure which utilizes an initial estimate to estimate the coefficients in a linear mode is introduced. A linear model containing replications with the same input matrix is considered and a class of two-stage estimators is defined. Optimal estimators in the class are determined, and it is shown that the proposed estimators are better than the corresponding single-sample least squares estimator if the initial estimate is accurate, and are as good (asymptotically) as the…

一个变量无法直接观测时的相关系数估计AI 解读

The problem is to estimate p the true correlation between two variables x and y when y and u=f(x) (but not x) are measured where f is a monotone function. The monotone correlation coefficient between y and u is defined and shown to be a (strongly) consistent estimator of p. For large samples approximate lower and upper bounds on the sample correlation between x and y are presented. Finally, an application of these results is displayed.