[Home ] [Archive]    
Main Menu
Journal Information::
Home::
Archive::
For Authors::
For Reviewers::
Principles of Transparency::
Contact us::
::
Search in website

Advanced Search
..
Committed to

AWT IMAGE

Attribution-NonCommercial
CC BY-NC


AWT IMAGE

Open Access Publishing


AWT IMAGE

Prevent Plagiarism

..
Registered in


..
Statistics
Journal volumes: 17
Journal issues: 34
Articles views: 645775
Articles downloads: 299456

Total authors: 581
Unique authors: 422
Repeated authors: 159
Repeated authors percent: 27

Submitted articles: 368
Accepted articles: 266
Rejected articles: 25
Published articles: 219

Acceptance rate: 72.28
Rejection rate: 6.79

Average Time to Accept: 282 days
Average Time to First Review: 27.2 days
Average Time to Publish: 26.1 days

Last 3 years statistics:
Submitted articles: 77
Accepted articles: 52
Rejected articles: 12
Published articles: 24

Acceptance rate: 67.53
Rejection rate: 15.58

Average Time to Accept: 201 days
Average Time to First Review: 11.4 days
Average Time to Publish: 139.4 days
____
..
:: Volume 10, Issue 2 (3-2014) ::
JSRI 2014, 10(2): 125-146 Back to browse issues page
An Empirical Comparison of Performance of the Unified Approach to Linearization of Variance Estimation after Imputation with Some Other Methods
Shahrzad Khatibi Nouri 1, Haminreza Navvabpour
1- , khtbnr.sh@gmail.com
Abstract:   (3570 Views)

Imputation is one of the most common methods to reduce item non_response effects. Imputation results in a complete data set, and then it is possible to use naϊve estimators. After using most of common imputation methods, mean and total (imputation estimators) are still unbiased. However their variances (imputation variances) are underestimated by naϊve variance estimators. Sampling mechanism and response variable values are variation sources which have been hidden in naϊve variance estimators. While missing mechanism and imputation processes are other sources which are created after imputation. The naϊve estimator does not account for these new variation sources. In this paper, a recent method of unified approach to linearization imputation variance estimation is explained. In this method, imputation estimator is linearized with respect to nuisance parameters estimators. Then linear estimator is asymptotically equal to imputation estimator. Variance estimators are also asymptotically equal. The unified approach can cover all deterministic and stochastic imputation methods, except nearest neighbors method. By a simulation study, imputation variance estimators of multiple imputation, model-assisted, bootstrap and unified approach are compared when regression imputation has been implemented. Performance of the imputation variance estimators are compared with respect to relative efficiency and coverage probability. Findings of the study show that unified approach and model_assisted are close in values of efficiencies and give more stable results through either increasing sample size or non-response rate.

Keywords: Non_response, multiple imputation, imputation variance, missing mechanism, quasi-likelihood, model-assisted method, bootstrap, linearization, reverse approach.
Full-Text [PDF 473 kb]   (972 Downloads)    
Type of Study: Research | Subject: General
Accepted: 2015/12/10 | Published: 2015/12/10
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Khatibi Nouri S, Navvabpour H. An Empirical Comparison of Performance of the Unified Approach to Linearization of Variance Estimation after Imputation with Some Other Methods. JSRI 2014; 10 (2) :125-146
URL: http://jsri.srtc.ac.ir/article-1-45-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 10, Issue 2 (3-2014) Back to browse issues page
مجله‌ی پژوهش‌های آماری ایران Journal of Statistical Research of Iran JSRI
Persian site map - English site map - Created in 0.05 seconds with 42 queries by YEKTAWEB 4645