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The frequencies of "m" independent "p-way" contingency tables are analyzed by a model that assumes that the ordinal category data in each of "m" groups are generated from a latent continuous multivariate normal distribution. The model permits analysis of several groups of individuals simultaneously. (TJH)

Confirmatory factor analysis is considered from a Bayesian viewpoint, in which prior information concerning parameters is incorporated in the analysis. An interactive algorithm is developed to obtain the Bayesian estimates. A numerical example is presented. (Author/JKS)

ABSTRACT: Aim: Screening algorithms for chronic kidney disease have been developed and validated in American populations. Given the worldwide burden of kidney disease, developing algorithms for populations outside the USA is needed. Methods: Using simple, non-invasive questions, we developed a prediction model for chronic kidney disease from national population samples in Korea. The Korean National Health and Nutrition Examination Survey ( n = 6565) was used for model development while validation was performed in two independent population samples, internal ( n = 2921) and external datasets ( n = 8166). Chronic kidney disease was defined as glomerular filtration rate < 60 mL/min per 1.73 m2. Results: Seven factors - age, female gender, anaemia, hypertension, diabetes mellitus, cardiovascular disease and proteinuria - were significantly associated with prevalent chronic kidney disease. Integer scores were assigned to variables based on the magnitude of associations: 2 for age 50-59 years, 3 for age 60-69 years and 4 for age 70 years or older, and 1 for female gender, anaemia, hypertension, diabetes, proteinuria and cardiovascular disease. Based on the Youden index, a value of 4 or greater defined a high risk population with sensitivity 89%, specificity 71%, and positive predictive value 19%, and negative predictive value 99%. The area under the curve was 0.83 for the development set, and 0.87 and 0.78 in the two validation datasets. Conclusion: This prediction algorithm, weighted towards common non-invasive variables, had good performance characteristics in an Asian population, and provides new evidence of the similarity of the algorithms for Western and Eastern populations. [ABSTRACT FROM AUTHOR]

Influence analysis is an important component of data analysis, and the local influence approach has been widely applied to many statistical models to identify influential observations and assess minor model perturbations since the pioneering work of Cook (1986). The approach is often adopted to develop influence analysis procedures for factor analysis models with ranking data. However, as this well-known approach is based on the observed data likelihood, which involves multidimensional integrals, directly applying it to develop influence analysis procedures for the factor analysis models with ranking data is difficult. To address this difficulty, a Monte Carlo expectation and maximization algorithm (MCEM) is used to obtain the maximum-likelihood estimate of the model parameters, and measures for influence analysis on the basis of the conditional expectation of the complete data log likelihood at the E-step of the MCEM algorithm are then obtained. Very little additional computation is needed to compute the influence measures, because it is possible to make use of the by-products of the estimation procedure. Influence measures that are based on several typical perturbation schemes are discussed in detail, and the proposed method is illustrated with two real examples and an artificial example. [ABSTRACT FROM AUTHOR]

By regarding the latent random vectors as hypothetical missing data and based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm, we investigate assessment of local influence of various perturbation schemes in a nonlinear structural equation model. The basic building blocks of local influence analysis are computed via observations of the latent variables generated by the Metropolis-Hastings algorithm, while the diagnostic measures are obtained via the conformal normal curvature. Seven perturbation schemes, including some perturbation schemes on latent vectors, are investigated. The proposed procedure is illustrated by a simulation study and a real example. [ABSTRACT FROM AUTHOR]

The full information item factor (FIIF) model is very useful for analyzing relations of dichotomous variables. In this article, we present a feasible procedure to assess local influence of minor perturbations for identifying influence aspects of the FIIF model. The development is based on a Q-displacement function which is closely related with the Monte Carlo EM algorithm in the ML estimation. In the E-step of this algorithm, the conditional expectations are approximated by sample means of observations simulated by the Gibbs sampler from the appropriate conditional distributions. It turns out that these observations can be utilized for computing the building blocks of the proposed diagnostic measures. The diagnoses are based on the conformal normal curvature that can be computed easily. A number of interesting perturbation schemes are considered. The methodology is illustrated with two real examples. [ABSTRACT FROM AUTHOR]

Recently, it has been recognized that the commonly used linear structural equation model is inadequate to deal with some complicated substantive theory. A new nonlinear structural equation model with fixed covariates is proposed in this article. A procedure, which utilizes the powerful path sampling for computing the Bayes factor, is developed for model comparison. In the implementation, the required random observations are simulated via a hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm. It is shown that the proposed procedure is efficient and flexible; and it produces Bayesian estimates of the parameters, latent variables, and their highest posterior density intervals as by-products. Empirical performances of the proposed procedure such as sensitivity to prior inputs are illustrated by a simulation study and a real example. [ABSTRACT FROM AUTHOR]

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 potential to assess a variety of complicated models that cannot be handled by existing methods. An application to the generalized linear mixed model is investigated. Some illustrative artificial and real examples are presented. [ABSTRACT FROM AUTHOR]