English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. In this paper we compare two different methods of estimating the errorvariances of two or more independent data sets. One method, called the“three-cornered hat” (3CH) method, requires three data sets. Anothermethod, which we call the “two-cornered hat” (2CH) method, requires onlytwo data sets. Both methods have been used in previous studies to estimatethe error variances associated with a number of physical and geophysical datasets. A key assumption in both methods is that the errors of the data setsare not correlated, although some studies have considered the effect of thepartial correlation of representativeness errors in two or more of the datasets. We compare the 3CH and 2CH methods using a simple model to simulatethree and two data sets with various error correlations andbiases. With this model, we know the exact error variances andcovariances, which we use to assess the accuracy of the 3CH and 2CHestimates. We examine the sensitivity of the estimated error variancesto the degree of error correlation between two of the data sets aswell as the sample size. We find that the 3CH method is less sensitiveto these factors than the 2CH method and hence is more accurate. Wealso find that biases in one of the data sets has a minimal effect onthe 3CH method, but can produce large errors in the 2CH method.

Evaluating two methods of estimating error variances using simulated data sets with known errors