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Showing 4 results for Subject:
Roshanak Aliakbari Saba, Alireza Zahedian, Marzieh Arbabi,
Volume 9, Issue 1 (9-2015)
Abstract
Annual estimation of average household incomes is one of the main goals of the household income and expenditure survey in Iran. So, regarding importance of accuracy of gathered data and reasons that lead to error in measuring household income, in this paper, model-based methods are used for estimating income measurement error and adjusting sample households declared income for 2011 household income and expenditure survey.
Meysam Tasallizadeh Khemes, Zahra Rezaei Ghahroodi,
Volume 11, Issue 2 (3-2018)
Abstract
There are several methods for clustering time course gene expression data. But, these methods have limitations such as the lack of consideration of correlation over time and suffering of high computational. In this paper, by introducing the non-parametric and semi parametric mixed effects model, this correlation over time is considered and by using penalized splines, computation burden dramatically reduced. At the end, using a simulation study the performance of the presented method is compared with previous methods and by using BIC criteria, the most appropriate model is selected. Also the proposed approach is illustrated in a real time course gene expression data set.
Roshanak Aliakbari Saba, Nasrin Ebrahimi, Lida Kalhori Nadrabadi, Asieh Abbasi,
Volume 15, Issue 2 (3-2022)
Abstract
The ranked set sampling method uses the ranking information of the units to provide a more representative sample of the population to the survey designers. The sampling distribution is closer to the actual distribution of the population. In this article, to ensure the effectiveness of ranked set sampling in extensive surveys conducted to prepare official statistics, we intend to use this sampling method to improve the efficiency of key estimates of household expenditure and income survey of the Statistics Center of Iran. The results show that using ranked set sampling to design household expenditure and income surveys can improve the efficiency of key estimates of the study, provided that the ranking variable used has a high correlation with the main variables of the study. Obviously, in the absence of a suitable and available variable for ranking the units, the information of the sampling frame can be used to construct a ranking variable correlated with the key variables of the survey.
Lida Kalhori Nadrabadi, Zohreh Fallah Mohsekhani,
Volume 16, Issue 1 (9-2022)
Abstract
In countries where labor force surveys are based on rotation samples and partially standard sample units at different periods, the number of changing statuses can be estimated and presented as flow statistics. The response error is one of the essential non-sampling errors in labor force statistics. This error is doubled in flow statistics. Usually, the error of classifying flow statistics is estimated using the interview method, which is costly and complex. This paper presents the process of estimating flow statistics and appropriate models for calculating the classification error for it. Also, according to Iran's sample rotation pattern, each model's feasibility is examined. Finally, the Markov latent class model, assuming inequality of transition probabilities based on the rotation pattern of Iran for labor force samples, is introduced as a fit model for estimating classification error for flow statistics in Iran using the labour force survey data of 2019 and 2020.
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