نشریه بررسی های آمار رسمی ایران
سال بیست و دوم شماره 1 (پیاپی 78، بهار و تابستان 1390)

In this article, we are going to interpret one of the queueing systems with a single server that has general and unknown distribution for the service and interarrival time using Bayesian view point.These General distributions may be well approximated by family of Mixed General Erlang distribution (MGE). This distribution is reparametrized such that it is possible to define a non-informative prior which allows using the MCMC method for estimating the parameters of models.Using the results of simulating parameters, we can estimate interesting measures that are based on recent results for known parameters in GI/G/1 frequently implemented queueing systems. At the end, we illustrate our approach for analyzing GI/G/1 systems with data from a bank.

Familiar model in the analysis of count data is Poisson which imposes the restrictive assumption that the variance equals the mean. This restriction is not usually satisfied in practice and thus model fitting process leads to invalid estimation results of parameters. This paper introduces the concept of over-distribution and its related tests, and presents several count data regression models, such as the negative binomial and the generalized Poisson, as alternatives to the Poisson model. Furthermore, some models for count data are compared under the assumption of having the same two first order moments. Also, we use the maximum likelihood approach to estimate the model parameters. Finally, we consider a real data set taken from the study of the impact of the metabolic syndrome among obese children in Isfahan. We analyze the number of white globules as responses and then select the best fitted model by using of some standard model choice criteria.

The main object of this paper is to introduce a method to identify outliers in multivariate data set. The robust method which is reviewed in this paper is Minimum Covariance Determinant (MCD). Then, two important characteristics of the robust estimators, i.e. the breakdown point and the influence function, are defined. We also present the consistency and finite–sample correction factors for the MCD estimator. Finally, it is shown that MCD method is better than a classical method for detecting outliers of a real data set.

In many statistical systems, a type of survey called “sampling over time” is common, which repeats continuously over time. One method of sampling over time is panel survey. Panel survey is a method in which a “fixed sample” is contacted during different waves. The most important challenge in a panel survey is debility in providing response from some sample units and having non-response that usually causes bias or in some cases leads to increasing variance of estimates. In addition to item nonresponse, in panel surveys, also there is another type of nonresponse called “wave nonresponse”. Wave nonresponse occurs when one or more waves of panel data are missing for a unit that has provided data for at least one wave. A particular aspect of wave nonresponse is attrition in which sampling unit is excluded from the survey. Weighting is a method of reducing undesirable effects of a wave nonresponse. Calibration estimator is one of the weighting methods. In this article after introducing primary concepts of panel survey, types of missingness in panel surveys and missing mechanisms, calibration estimators is introduced as a method of weighting missing data. Then by using of British Household Panel Survey data, calibration estimator method is compared with generalized regression estimator with respect to different criteria such as absolute relative bias, mean square error and estimated asymptotic relative efficiency. The findings of the study show that when correlation between two waves is high and missing mechanism is at random, calibration estimator method has better performance than some other methods.

In this article, the efficiency of balanced sampling is compared with that of PPS sampling under the same conditions, where in both methods, only one auxiliary variable is used. For this purpose, by performing a simulation study with different sampling fractions, the efficiency has been computed. The findings show that under the same conditions in terms of using auxiliary information, regardless of the size of sampling fraction, the use of balanced sampling leads to more gain than that of the PPS sampling and by increasing the sampling fraction, the gain is increased.

In most surveys we are dealing with characteristics which change over time. In these surveys we may be interested in estimating population parameters in regular time intervals and studying their changes from one period to the next. Due to expanding the use of rotation sampling in statistical centers, a comprehensive study about the variance estimation on rotation sampling schemes seems necessary. Rotation sampling is one of the methods used in repeated surveys. In this paper, we emphasis on two-way balanced one-level rotation designs and by use of U.S. consumer expenditure survey data we compare estimated variances for generalized composite estimator and simple mean estimator of consumer expenditure. Results of this study indicate the generalized composite estimate is more efficient than the simple mean estimate.

In this paper, we investigate the effects of the introduction of 5-year impact factors. We collect impact factor data for all disciplines available in Journal Citation Reports. For all these categories, we give insights into the relationship between the traditional 2-year impact factor and the new 5-year impact factor. Our main focus is to investigate whether the traditionally low impact factors for statistics journals improve with this new measure. The results show that the statistics discipline indeed benefits from the 5-year window, that is, the impact factor increases. This appears to be true for most disciplines, although the statistics discipline ranks among the top 15 (out of 171) disciplines in this respect.