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

Suppose a process yields independent observations whose distributions belongto a family parameterized by \theta\in\Theta. When the process is in control,the observations are i.i.d. with a known parameter value \theta_0. When theprocess is out of control, the parameter changes. We apply an idea of Robbinsand Siegmund [Proc. Sixth Berkeley Symp. Math. Statist. Probab. 4 (1972) 37-41]to construct a class of sequential tests and detection schemes whereby theunknown post-change parameters are estimated. This approach is especiallyuseful in situations where the parametric space is intricate and mixture-typerules are operationally or conceptually difficult to formulate. We exemplifyour approach by applying it to the problem of detecting a change in the shapeparameter of a Gamma distribution, in both a univariate and a multivariatesetting.

Nonanticipating estimation applied to sequential analysis and changepoint detection