The minimum coverage probability of confidence intervals in regression after a preliminary F test

Host Institution:

La Trobe University

Title of Seminar:

The minimum coverage probability of confidence intervals in regression after a preliminary F test

Speaker's Name:

Associate Professor Paul Kabaila

Speaker's Institution:

La Trobe University

Time and Date:

Friday 24th February, 2012, 11:00 AM (AEDT)

Seminar Abstract:

In the applied statistics literature on the one-way analysis of covariance, it is commonly recommended that a preliminary F test of the null hypothesis of "parallelism" be carried out. Preliminary F tests are also recommended in other linear regression model scenarios. Let s denote the number of parameters that are assumed to be zero in the description of the null hypothesis for this F test. Our aim is to find the coverage probability properties of a confidence interval for a specified linear combination of the regression parameters, constructed after a preliminary F test and based on the assumption that the selected model had been given to us a priori (as the true model). We describe a new elegant method for computing the minimum coverage probability of this confidence interval, that works well irrespective of how large s is.

Reference:  Kabaila, P. & Farchione, D. (2012). The minimum coverage probability of confidence intervals in regression after a preliminary F test. Journal of Statistical Planning and Inference, 142, 956-964.

Seminar Convenor:

This email address is being protected from spambots. You need JavaScript enabled to view it.

AGR IT support:

This email address is being protected from spambots. You need JavaScript enabled to view it.