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

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

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 Comparison of Variance Estimators for Systematic Environmental Sample Surveys: Considerations for Post-Stratified Estimation

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 772
Author(s):  
Bryce Frank ◽  
Vicente J. Monleon
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
National Forest Inventory ◽  
Point Estimation ◽  
Systematic Sampling ◽  
Sampling Variance ◽  
Variance Estimators ◽  
The Past ◽  
Point Estimators ◽  
Species Specific

The estimation of the sampling variance of point estimators under two-dimensional systematic sampling designs remains a challenge, and several alternative variance estimators have been proposed in the past few decades. In this work, we compared six alternative variance estimators under Horvitz-Thompson (HT) and post-stratification (PS) point estimation regimes. We subsampled a multitude of species-specific forest attributes from a large, spatially balanced national forest inventory to compare the variance estimators. A variance estimator that assumes a simple random sampling design exhibited positive relative bias under both HT and PS point estimation regimes ranging between 1.23 to 1.88 and 1.11 to 1.78 for HT and PS, respectively. Alternative estimators reduced this positive bias with relative biases ranging between 1.01 to 1.66 and 0.90 to 1.64 for HT and PS, respectively. The alternative estimators generally obtained improved efficiencies under both HT and PS, with relative efficiency values ranging between 0.68 to 1.28 and 0.68 to 1.39, respectively. We identified two estimators as promising alternatives that provide clear improvements over the simple random sampling estimator for a wide variety of attributes and under HT and PS estimation regimes.

scholarly journals On a general class of long run variance estimators

2013 ◽  
Vol 120 (3) ◽  
pp. 437-441 ◽  
Author(s):  
Xianyang Zhang ◽  
Xiaofeng Shao
Keyword(s):  
General Class ◽  
Variance Estimators ◽  
Long Run

Combining standardized time series area and Cramér-von Mises variance estimators

2007 ◽  
Vol 54 (4) ◽  
pp. 384-396 ◽  
Author(s):  
David Goldsman ◽  
Keebom Kang ◽  
Seong-Hee Kim ◽  
Andrew F. Seila ◽  
Gamze Tokol
Keyword(s):  
Time Series ◽  
Variance Estimators ◽  
Von Mises ◽  
Cramer Von Mises
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