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Measuring a binary response's range of influence in logistic regression.

Authors
Landsittel-D; Singh-H; Arena-VC; Anderson-SJ
Source
Am Stat 2002 Nov; 56(4):337-342
NIOSHTIC No.
20023019
Abstract
The recent article by Fay in which he proposed the range of influence (ROI) statistic for logistic regression, provides a useful diagnostic approach for assessing an observation's "potential influence" on the predicted value (or other statistic of interest). We agree with the author's conclusion that results of this procedure may add substantial information to existing diagnostics. This article was of particular interest to us since we have been investigating the same quantity in a completely different context, namely quantifying degrees of freedom for neural networks and other complex modeling procedures. The primary purpose of this letter is to show how the ROI statistic related to the concept of "generalized degrees of freedom" as developed by Ye (1998). In the special case of logistic regression, simulation results indicate that the absolute value of the ROI statistic asymptotically corresponds to the diagonal of the hat matrix.
Keywords
Simulation-methods; Mathematical-models; Models; Statistical-analysis; Sampling
CODEN
ASTAAJ
Publication Date
20021101
Document Type
Other
Fiscal Year
2003
NTIS Accession No.
NTIS Price
Issue of Publication
4
ISSN
0003-1305
NIOSH Division
HELD
Source Name
The American Statistician
State
PA; WV
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