A comparison of 20 heterogeneity variance estimators in statistical synthesis of results from studies: a simulation study

2017 ◽  
Vol 36 (27) ◽  
pp. 4266-4280 ◽  
Author(s):  
Maria Petropoulou ◽  
Dimitris Mavridis
Keyword(s):  
Simulation Study ◽  
Variance Estimators ◽  
Heterogeneity Variance

An empirical comparison of heterogeneity variance estimators in 12 894 meta-analyses

2015 ◽  
Vol 6 (2) ◽  
pp. 195-205 ◽  
Author(s):  
Dean Langan ◽  
Julian P. T. Higgins ◽  
Mark Simmonds
Keyword(s):  
Empirical Comparison ◽  
Variance Estimators ◽  
Heterogeneity Variance ◽  
Meta Analyses

A comparison of heterogeneity variance estimators in combining results of studies

2006 ◽  
Vol 26 (9) ◽  
pp. 1964-1981 ◽  
Author(s):  
Kurex Sidik ◽  
Jeffrey N. Jonkman
Keyword(s):  
Variance Estimators ◽  
Heterogeneity Variance

scholarly journals Robust Brown-Forsythe and Robust Modified Brown-Forsythe ANOVA Tests Under Heteroscedasticity for Contaminated Weibull Distribution

2016 ◽  
Vol 39 (1) ◽  
pp. 17-32 ◽  
Author(s):  
Derya Karagöz ◽  
Tülay Saraçbasi
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Simulation Study ◽  
Data Sets ◽  
Variance Estimators ◽  
Robust Tests ◽  
Mean And Variance ◽  
Two Parameters

<p>In this study, robust Brown-Forsythe and robust Modified Brown-Forsythe ANOVA tests are proposed to take into consideration heteroscedastic and non-normality data sets with outliers. The non-normal data is assumed to be a two parameters Weibull distribution. Robust proposed tests are obtained by using robust mean and variance estimators based on median=MAD and median=Qn methods instead of maximum likelihood. The behaviors of the robust proposed and classical ANOVA tests are examined by simulation study. The results shows that the proposed robust tests have good performance especially in the presence of heteroscedasticity and contamination.</p>

scholarly journals L-Moments and Calibration-Based Estimators for Variance Parameter

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Malik Muhammad Anas ◽  
Muhammad Ali ◽  
Ambreen Shafqat ◽  
Faisal Shahzad ◽  
Kashif Abbass ◽  
...  
Keyword(s):  
Simulation Study ◽  
Random Sampling ◽  
Variance Estimation ◽  
Extreme Values ◽  
Natural Sciences ◽  
Stratified Random Sampling ◽  
Variance Estimators ◽  
Highly Efficient ◽  
L Moments ◽  
The Subject

The subject of variance estimation is one of the most important topics in statistics. It has been clarified by many different research studies due to its various applications in the human and natural sciences. Different variance estimators are built based on traditional moments that are especially influenced by the existence of extreme values. In this paper, with the presence of extreme values, we proposed some new calibration estimators for variance based on L-moments under double-stratified random sampling. A simulation study with COVID-19 data is performed to evaluate the efficiency of the proposed estimators. All results indicate that the proposed estimators are often superior and highly efficient compared to the existing traditional estimator.

scholarly journals A comparison of heterogeneity variance estimators in simulated random‐effects meta‐analyses

2018 ◽  
Vol 10 (1) ◽  
pp. 83-98 ◽  
Author(s):  
Dean Langan ◽  
Julian P.T. Higgins ◽  
Dan Jackson ◽  
Jack Bowden ◽  
Areti Angeliki Veroniki ◽  
...  
Keyword(s):  
Random Effects ◽  
Variance Estimators ◽  
Heterogeneity Variance ◽  
Meta Analyses

scholarly journals Simulation Studies for Complex Sampling Designs

2016 ◽  
Vol 35 (4) ◽  
Author(s):  
Helga Wagner ◽  
Doris Eckmair
Keyword(s):  
Simulation Study ◽  
Variance Estimation ◽  
Sampling Error ◽  
Estimation Method ◽  
Sampling Plan ◽  
Simulation Studies ◽  
Complex Surveys ◽  
Variance Estimators ◽  
Complex Sampling ◽  
Complex Sampling Designs

Choosing the appropriate variance estimation method in complex surveys is a difficult task since there exist a variety of techniques which usually cannot be compared mathematically. A relatively easy way to accomplish such a comparison is on the basis of simulation studies. Though simulation studies are widely used in statistics, they are not a standard tool for investigating properties of estimators in complex survey sampling designs. In this paper we describe the setup for a simulation study according to the sampling plan of the Austrian Microcensus (AMC), used 1994–2003 which is an example for a very complex sampling plan. To illustrate the proceeding we conducted a simulation study comparing basic variance estimators. Results of the study reveal the extent to which simple variance estimators may underestimate the true sampling error in close to reality situations.

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