Journal of Statistical Research of Iran
Volume:2 Issue: 2, 2005

In recent years, needs for small area estimations have been greatly increased for large surveys particularly household surveys in Statistical Centre of Iran (SCI), because of the costs and respondent burden. The lack of suitable auxiliary variables between two decennial housing and population census is a challenge for SCI in using these methods.In general, the small area estimators can be classified into three categories: direct estimators, indirect estimators, and their combination.The direct estimators are those estimators using just data falling into small areas for estimating parameter of interest in small areas. The indirect estimators use data collected from both small areas of interest and other areas to estimate the parameters.The small area estimators used in this paper are indirect estimators with a combination of direct and indirect estimators. Three well-known small area estimators, i.e. synthetic estimator, composite estimator, and adjusted regression estimator are introduced and calculated under various conditions. First, a linear systematic sample with a size of 15,400 was selected from active population in the 1996 Census at 0.95 confidence level and a 0.05 maximum relative error.To calculate quality indices in order to assess small area estimators, 1000 sample were selected from the population. Sample size in each province (small area) is a random variable since it varies in each replication. Since the employment information has been collected in the 1996 Census, the true unemployment rate is known for each province. The small area estimators use auxiliary variables from previous census or large scale surveys and because of the long intercensal period, auxiliary variables tend to be out of date. So, for evaluating the effect of out-of-date auxiliary variables on performance of small area estimators, auxiliary variables of 1986 Census were used. For this purpose we have used suitable auxiliary variables from 1986 Census file in each province. There are four quality measures for comparison of small area methods, including bias, mean squared error (MSE), average of relative errors (ARE), and average of squared errors (ASE). Using these measures, four estimators are chosen as the selected

p(C-Sy-Av): Composite estimator with synthetic estimator and mean of group weights, p(C-Al-Av): Composite estimator with synthetic alternative estimator and mean of group weights, p(JS-Sy): James-Stein estimator with synthetic estimator, and p(JS-Al): James-Stein estimator with synthetic alternative estimator. MSE charts show that if we assign a fixed value as a minimum sample size in each small area, we can always be assure to have acceptable MSE's in using the above methods. Also p(JS-Al) leads to smaller values of ARE and ASE, and in maximum relative error point of view, p(C-Al-Av) has smaller values than the other selected estimators.

Spectral analysis considers the problem of determining (the art of recovering) the spectral content (i.e., the distribution of power over frequency) of a stationary time series from a finite set of measurements, by means of either nonparametric or parametric techniques. This paper introduces the spectral analysis problem, motivates the definition of power spectral density functions, and reviews some important and new techniques in nonparametric and parametric spectral estimation. We also consider the problem in the context of multivariate time series.

A new test of inheritance, S, is proposed, which uses information from affected as well as unaffected siblings in the family. The siblings are analyzed in terms of similarities of haplotypes. The distribution of the proposed S-test is derived under the null hypothesis of random inheritance. Mean and variance are obtained for the distribution. The test is then applied to data sets published in the literature. The results suggest some sort of linkage between haplotypes and disease genes.

This paper aims to investigate the main socio-economic determinants of female age at first marriage in Chuadanga district, Bangladesh, over the year 2005, and attempts to find some explanations for the differences in the age at marriage among socio-economic characteristics. The findings of the multiple classification analysis (MCA) suggest that among all the variables analyzed in the study, education levels have a positive association with age at marriage. Age at marriage is increased with the level of husband's and wife's education. The MCA also indicates that female education has the strongest influence on age at marriage while husband's education is the second strongest determinant. The analysis reveals that the mean age at marriage of women is 16.77 years: 16.05 in rural areas and 18.22 in urban areas. It also shows that respondent's occupation and religion have an important significant contribution to female age at first marriage.