Further properties of frequentist confidence intervals in regression   that utilize uncertain prior information
  Consider a linear regression model with n-dimensional response vector, regression parameter \beta = (\beta_1, ..., \beta_p) and independent and identically N(0, \sigma^2) distributed errors. Suppose that the parameter of interest is \theta = a^T \beta where a is a specified vector. Define the parameter \tau = c^T \beta - t where c and t are specified. Also suppose that we have uncertain prior information that \tau = 0. Part of our evaluation of a frequentist confidence interval for \theta is the ratio (expected length of this confidence interval)/(expected length of standard 1-\alpha confidence interval), which we call the scaled expected length of this interval. We say that a 1-\alpha confidence interval for \theta utilizes this uncertain prior information if (a) the scaled expected length of this interval is significantly less than 1 when \tau = 0, (b) the maximum value of the scaled expected length is not too much larger than 1 and (c) this confidence interval reverts to the standard 1-\alpha confidence interval when the data happen to strongly contradict the prior information. Kabaila and Giri, 2009, JSPI present a new method for finding such a confidence interval. Let \hat\beta denote the least squares estimator of \beta. Also let \hat\Theta = a^T \hat\beta and \hat\tau = c^T \hat\beta - t. Using computations and new theoretical results, we show that the performance of this confidence interval improves as |Corr(\hat\Theta, \hat\tau)| increases and n-p decreases. 
Paul Kabaila and Khageswor Giri
stat.ME