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不完全数据模型的局部影响分析AI 解读

Summary This paper proposes a method to assess the local influence in a minor perturbation of a statistical model with incomplete data. The idea is to utilize Cook's approach to the conditional expectation of the complete-data log-likelihood function in the EM algorithm. It is shown that the method proposed produces analytic results that are very similar to those obtained from a classical local influence approach based on the observed data likelihood function and has the…

连续与多类别混合数据的潜变量模型AI 解读

Summary Owing to the nature of the problems and the design of questionnaires, discrete polytomous data are very common in behavioural, medical and social research. Analysing the relationships between the manifest and the latent variables based on mixed polytomous and continuous data has proven to be difficult. A general structural equation model is investigated for these mixed outcomes. Maximum likelihood (ML) estimates of the unknown thresholds and the structural parameters…

结构方程模型的多层分析AI 解读

This paper considers the multilevel analysis of structural equation models with unbal anced sampling designs. The analysis is based on the maximum likelihood and the generalized least squares approaches. Basic statistical results for inference such as the asymptotic distribution of the estimators, goodness-of-fit test statistics for the validity of the model and functional constraints are developed. Computationally, the applications of the scoring algorithm and the…

结构方程模型的多层分析AI 解读

This paper considers the multilevel analysis of structural equation models with unbal anced sampling designs. The analysis is based on the maximum likelihood and the generalized least squares approaches. Basic statistical results for inference such as the asymptotic distribution of the estimators, goodness-of-fit test statistics for the validity of the model and functional constraints are developed. Computationally, the applications of the scoring algorithm and the…

结构方程模型中函数约束的检验AI 解读

In the context of a robust generalized least squares approach, a new statistic for testing the validity of constraints in structural equation models is proposed. It is shown that the new test requires significantly less computational effort than the traditional one. Extension to models in several populations is also considered. Illustrative applications based on real data are given.

协方差结构分析中的约束估计AI 解读

In covarianoe structure analysis, the method of weighted least squares for estimating parameters that are subject to functional constraints is developed. Statistical properties of the estimator are studied and a goodness-of-fit statistic is presented. Based on the penalty function technique, an algorithm is developed. This algorithm is able to produce not only the constrained weighted least squares estimator but also the constrained maximum likelihood estimator, if we choose…

协方差结构分析中的约束估计AI 解读

In covarianoe structure analysis, the method of weighted least squares for estimating parameters that are subject to functional constraints is developed. Statistical properties of the estimator are studied and a goodness-of-fit statistic is presented. Based on the penalty function technique, an algorithm is developed. This algorithm is able to produce not only the constrained weighted least squares estimator but also the constrained maximum likelihood estimator, if we choose…