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

• Published in: Computational statistics & data analysis Vol. 51; no. 12; pp. 5537 - 5546
• Main Authors: O’Hara Hines, R.J., Hines, W.G.S.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.08.2007; Elsevier Science; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Ambiguous influence diagnostics; Calculus of variations and optimal control; Exact sciences and technology; General topics; Local perturbations; Mathematical analysis; Mathematics; Missing at random; Missing responses; Missing-not-at-random; Multivariate analysis; Non-ignorable drop-outs; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Sciences and techniques of general use; Statistics; Local perturbations; Non-ignorable drop-outs; Ambiguous influence diagnostics; Missing at random; Missing-not-at-random; Missing responses; Bad specification; Data analysis; Sensitivity analysis; Multivariate analysis; Local influence; Statistical computation; Covariance
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2007.03.027

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.