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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Because the weighted total least squares (WTLS) method lacks robustness and is sensitive to gross errors, it cannot eliminate the influence of outliers effectively. A small number of gross errors may produce a devastating effect on estimates. Focusing on the limitation of the WTLS method regarding the influence of gross errors, this work combines Jackknife resampling theory with the WTLS algorithm for the identification and detection of outliers. The gross errors located in the WTLS method by the Jackknife method are identified to further improve the qualities of the estimated values if the observation data are falsified by outliers. This paper focuses on the following two aspects: only one gross error and multiple gross errors. Detailed calculation steps and the whole procedure for outlier detection using the new method are given. This algorithm is applied to the straight-line fitting model and the plane coordinate transformation model. From the experimental estimation results, we can see that the method proposed in this paper can identify gross errors that are greater than or equal to three times the standard error, and obtain more accurate estimation values when compared with the WTLS method and the classic robust weighted total least squares (RWTLS) method. The numerical case studies verify the effectiveness and practicality of the proposed procedure.

Jackknife method for the location of gross errors in weighted total least squares