[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Ethics Considerations::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
Indexing and Abstracting



 
..
Social Media

..
Licenses
Creative Commons License
This Journal is licensed under a Creative Commons Attribution NonCommercial 4.0
International License
(CC BY-NC 4.0).
 
..
Similarity Check Systems


..
:: Search published articles ::
Showing 2 results for Kalhori Nadrabadi

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.


Page 1 from 1     

مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

Persian site map - English site map - Created in 0.05 seconds with 34 queries by YEKTAWEB 4710