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

We consider a linear regression model with regression parameter beta=(beta_1, ..., beta_p) and independent and identically N(0, sigma^2)distributederrors. Suppose that the parameter of interest is theta = a^T beta where a is aspecified vector. Define the parameter tau = c^T beta - t where the vector cand the number t are specified and a and c are linearly independent. Alsosuppose that we have uncertain prior information that tau = 0. Kabaila and Giri(2009c) present a new frequentist 1-alpha confidence interval for theta thatutilizes this prior information. This interval has expected length that (a) isrelatively small when the prior information about tau is correct and (b) has amaximum value that is not too large. It coincides with the standard 1-alphaconfidence interval (obtained by fitting the full model to the data) when thedata strongly contradicts the prior information. At first sight, thecomputation of this new confidence interval seems to be infeasible. However, bythe use of the various computational devices that are presented in detail inthe present paper, this computation becomes feasible and practicable.

Computation of confidence intervals in regression utilizing uncertain prior information