Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs

Authors:
R. J. O'Hara Hines;W. G. S. Hines
Affiliations:
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ont., Canada N2L 3G1;Department of Mathematics and Statistics, University of Guelph, Guelph, Ont., Canada N1G 2W1
Venue:
Computational Statistics & Data Analysis
Year:
2007

Citing 3
Cited 1

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Abstract

Our work examines the performance of proposed local influence diagnostics applied to multivariate normal longitudinal data with drop-outs: these diagnostics prove to be ambiguous as they are sensitive not only to the presence of anomalous records, as intended, but also, unfortunately, to the misspecification of the longitudinal covariance structure of the response. We suggest an unambiguous index for detecting covariance misspecification, and recommend that an analyst use this index first to confirm that the covariance structure is well specified before attempting to interpret the influence diagnostics.