Handbook of latent variable and related models

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques

Estimation of covariance structure models with parameters subject to functional restraints

confirmatory factor analysis, covariance structure analysis, equality constraints, Gauss-Newton algorithm, Heywood case, inequality constraints, penalty function, quasi-Weiner simplex model, Scoring algorithm,

Structural equation modeling : a bayesian approach/ Sik

xv, p. 432: ill.; 28 c

Variable bandwidth selection in varying-coefficient models

The varying-coefficient model is an attractive alternative to the additive and other models. One important method in estimating the coefficient functions in this model is the local polynomial fitting approach. In this approach, the choice of bandwidth is crucial. If the unknown curve is spatial homogeneous, a constant bandwidth is sufficient. However, for estimating curves with a more complicated structure, a variable bandwidth is needed. The present article focuses on a variable bandwidth selection procedure, and provides the conditional bias and the conditional variance of the estimator, the convergence rate of the bandwidth, and the asymptotic distribution of its error relative to the theoretical optimal variable bandwidth

ASYMPTOTIC THEORY OF TWO-LEVEL STRUCTURAL EQUATION MODELS WITH CONSTRAINTS

Abstract: In the context of a general two-level structural equation model with an unbalanced design and small samples at the individual levels, maximum likelihood theory is developed for estimation of the unknown parameters subject to functional constraints. It is shown that the constrained maximum likelihood estimates are consistent and asymptotically normal. A goodness-of-fit statistic is established to test the validity of the constraints. The asymptotic results are illustrated with an example

Asymptotic theory of two-level structuralequation model with constrained conditions

In the context of a general two-level structural equation model with an unbalanced design and small samples at the individual levels, maximum likelihood theory is developed for estimation of the unknown parameters subject to functional constraints. It is shown that the constrained maximum likelihood estimates are consistent and asymptotically normal. A goodness-of-fit statistic is established to test the validity of the constraints. The asymptotic results are illustrated with an exampl