Inverse-variance weighting

From Wikipedia, the free encyclopedia
Jump to: navigation, search

In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average. Each random variable is weighted in inverse proportion to its variance.

Given a sequence of independent observations yi with variances σi2, the inverse-variance weighted average is given by[1]

 \hat{y} = \frac{\sum_i y_i / \sigma_i^2}{\sum_i 1/\sigma_i^2} .

The inverse-variance weighted average has the least variance among all weighted averages, which can be calculated as

 D^2(\hat{y}) = \frac{1}{\sum_i 1/\sigma_i^2} .

If the variances of the measurements are all equal, then the inverse-variance weighted average becomes the simple average.

Inverse-variance weighting is typically used in statistical meta-analysis to combine the results from independent measurements.

See also[edit]

References[edit]

  1. ^ Joachim Hartung, Guido Knapp, Bimal K. Sinha (2008). Statistical meta-analysis with applications. John Wiley & Sons. ISBN 978-0-470-29089-7.