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Robust Confidence Intervals for Regression Parameters
Author(s) -
Field Christopher A.,
Welsh A.H.
Publication year - 1998
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/1467-842x.00006
Subject(s) - mathematics , statistics , confidence interval , robustness (evolution) , robust confidence intervals , computation , regression , regression analysis , bounded function , confidence region , econometrics , algorithm , mathematical analysis , biochemistry , chemistry , gene
The paper considers the problem of finding accurate small sample confidence intervals for regression parameters. Its approach is to construct conditional intervals with good robustness characteristics. This robustness is obtained by the choice of the density under which the conditional interval is computed. Both bounded influence and S‐estimate style intervals are given. The required tail area computations are carried out using the results of DiCiccio, Field & Fraser (1990).