JOURNAL OF STATISTICAL SCIENCES
Year:2010 | Volume:4 | Issue:1|Papers Count:7

AIC is commonly used for model selection but the value of AIC has no direct interpretation Cox's test is a generalization of the likelihood ratio test  When the true model is unknown  based on AIC we select  a model but we cannot talk about the closeness of  the selected model to the true model Because it is not clear the selected model is wellspecified or mis-specified This paper extends Akaikes AIC-type model selection beside the Cox test for model selection and based on the simulations we study the results of AIC and Cox's test and the ability of these two criterion and test to discriminate models If based on AIC we select a model whether or not Cox's test has a ability of selecting a better model  Words which one will considering the foundations of the rival models On the other hand the model selection literature has been generally poor at reflecting the foundations of a set of reasonable models when the true model is unknown As a part of results we will propose an approach to selecting the reasonable set of models.

In this paper, we characterize symmetric distributions based on Renyi entropy of order statistics in subsamples. A test of symmetry is proposed based on the estimated Renyi entropy. Critical values of the test are computed by Monte Carlo simulation. Also we compute the power of the test under different alternatives and show that it behaves better that the test of Habibi and Arghami (1386).

The multilevel models are used in applied sciences including social sciences, sociology, medicine, economic for analysing correlated data. There are various approaches to estimate the model parameters when the responses are normally distributed. To implement the Bayesian approach, a generalized version of the Markov Chain Monte Carlo algorithm, which has a simple structure and removes the correlations among the simulated samples for the fixed parameters and the errors in higher levels, is used in this article. Because the dimension of the covariance matrix for the new error vector is increased, based upon the Cholesky decomposition of the covariance matrix, two methods are proposed to speed the convergence of this approach. Then, the performances of these methods are evaluated in a simulation study and real life data.

The GARCH(1,1) and GARCH(1,1)-t models lead to highly volatile quantile forecasts, while historical simulation, Variance–Covariance, adaptive generalized Pareto distribution and non-adaptive generalized Pareto distribution models provide more stable quantile forecasts. In general, GARCH(1,1)-t, generalized Pareto distribution models and historical simulation are preferable for most quantiles.

This paper is devoted to compute the sample size for estimation of Normal distribution mean with Bayesian approach. The Quadratic loss function is considered and three criterions are applied to obtain p- tolerance regions with the lowest posterior loss. These criterions are: average length, average coverage and worst outcome. The proposed methodology is examined, and its effectiveness is shown.

The main part of optimal designs in the mixed effects models concentrates on linear models and binary models. Recently, Poisson models with random effects have been considered by some researchers. In this paper, an especial case of the mixed effects Poisson model, namely Poisson regression with random intercept is considered. Experimental design variations are obtained in terms of the random effect variance and indicated that the variations depend on the variance parameter. Using D-efficiency criterion, the impression of random effect on the experimental setting points is studied. These points are compared with the optimal experimental setting points in the corresponding model without random effect. We indicate that the D-efficiency depends on the variance of random effect.

Parallel to social developments and increasing use of statistics in planning, demand for producing and disseminating social and economic information have been risen dramatically. Survey sampling is one of the methods of gathering such information. The quality of survey data is important for planners and researches. In recent years, the concept of response burden has been expressed as one of the new components of the data quality. Unfortunately, In National Statistical System of Iran nothing has been done to evaluate response burden in order to improve survey data quality. In this paper, we introduce existing methods of measuring response burden and express other countries experiences to assess perceived response burden in surveys. A common approach to assess response burden is designing a Perceived Response Burden survey and carry it out along with the host survey. In order to illustrate how to design, conduct and analyze results of this survey, a Perceived Response Burden survey which has been conducted in Faculty of Economic, Allameh Tabatabaei University, to measure response burden impose on respondents in Health Survey is presented.