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LOESS smoothed density estimates for multivariate survival data subject to censoring and masking

Published online by Cambridge University Press:  22 August 2016

Peter Adamic*
Laurentian University, Sudbury, Ontario P3E 2C6, Canada
Jenna Guse
University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
*Correspondence to: Peter Adamic, Laurentian University, 935 Ramsey Lake Rd, Sudbury, Ontario P3E 2C6, Canada. Tel: (705)675-1151 x2325; E-mail:


Actuaries often encounter censored and masked survival data when constructing multiple-decrement tables. In this paper, we propose estimators for the cause-specific failure time density using LOESS smoothing techniques that are employed in the presence of left-censored data, while still allowing for right-censored and exact observations, as well as masked causes of failure. The smoothing mechanism is incorporated as part of an expectation-maximisation algorithm. The proposed models are applied to a bivariate African sleeping sickness data set.

© Institute and Faculty of Actuaries 2016 

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