نشریه اندیشه آماری
سال دهم شماره 2 (پیاپی 20، پاییز و زمستان 1384)

Iran is implementing its Fourth Five-year Economic, Social and Cultural Development Plan. Therefore, the National Statistical System (NSS) has to fulfill emerging statistical demands required by the plan. This issue has led the NSS to face some challenges. Due to this fact, currently some measures have been adopted to improve the current status and a plan is being conducted to meet the new statistical needs. This paper review the history of establishment of the NSS in Iran and the challenges facing the current NSS. The four steps, shaping the process of revising the current NSS for achieving a dynamic and optimal system, will be next discussed. In this connection, nine working groups were initiated in the Statistical Centre of Iran (SCI) to review the current NSS and to develop the required plan. Finally, the probable obstacles in the way of implementing the Development Plan of the NSS in Iran are discussed.

In Practical situations, the necessity of using a richer class of the classic discrete probability distributions, such as the negative binomial and Poisson distributions, is felt. In this article, by entering an additional parameter ρ, we extend these classic distributions to the class of inflated-parameter distributions. We show that these generalized distributions are suitable in modeling correlated count data with superdispersion. In particular, we illustrate this by applying them to some set of real data concerning number of claims in an insurance company. Finally, important characteristics of these distributions are compared with those of the corresponding classic distributions.

In this paper, the balanced loss function (BLF) which reflects both the goodness of fit and the precision of estimation are considered. The estimation of a normal mean and the admissibility of all linear estimations of the sample mean X are studied.

A mathematical model is proposed for the description of the T4 level, as a function of the since initial infection, in the blood of an HIV-infected individual. Two random variables are defined for each infected individual, the time T from the moment of infection to the moment when the infection first discovered, and level R of T4 cells in the blood at the time of discovery. Here T is the length of the latency period, and is not typically observable. On the basis of a stochastic model employing a stationary Gaussian process, the joint density of this two random variables is derived, and hence the conditional distribution of T. given R. It is shown that if T has an exponential marginal distribution, then the conditional distribution of T is a censored normal distribution. The theory is applied to data collected from intravenous drug users in New York City. We applied this theory to real data of infected individual in Kermanshah and estimated this model on this data.