Abstract
A highest posterior density interval for examining shifts in individual regression coefficients between subsamples with different error variances is derived from a weighted-t posterior density. Compared to a double-t approach, the weighted-t approach is computationally more efficient and allows a direct comparison to a sampling theory approach. In a numerical example the weighted-t, double-t and confidence interval approaches are compared.
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