دانلود گزارش کارورزی و کار آموزی

چکیده : 

In the behavioral, social, psychological, and the medical sciences, the most widely used

models in assessing latent variables are the structural equation models (SEMs). However,

most of the existing statistical methods for analyzing SEMs have been developed for

normally distributed data. Transformation SEMs are useful tools for tackling the nonnormality

of multidimensional data and simultaneously revealing the interrelationships

among latent variables. The main objective of this paper is to develop a Bayesian

diagnostic procedure for transformation SEMs. The first- and second-order local inference

measures are established with the objective functions that are defined based on the

logarithm of Bayes Factor. Markov chain Monte Carlo (MCMC) methods with the Bayesian

P-splines approach are developed to compute the local influence measures and to

estimate nonparametric transformations, latent variables, and unknown parameters.

Compared with conventional maximum likelihood-based diagnostic procedures, the

Bayesian diagnostic approach could detect outliers and/or influential points in the observed

data, as well as conduct model comparison and sensitivity analysis through various

perturbations of the data, sampling distributions, or prior distributions of the model

parameters. Simulation studies reveal the empirical performance of the proposed Bayesian

diagnostic procedure. An actual data set that is extracted from a study based on the Hong

Kong Diabetes Registry is used to illustrate the application of our methodology