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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  and
Jenna Guse
Peter Adamic*
Affiliation:
Laurentian University, Sudbury, Ontario P3E 2C6, Canada
Jenna Guse
Affiliation:
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: padamic@laurentian.ca
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Abstract

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.

Keywords

LOESS smoothingMultiple decrementLeft censoringRight censoring
Type
Papers
Information
Annals of Actuarial Science , Volume 10 , Issue 2 , September 2016 , pp. 285 - 302
Copyright
© Institute and Faculty of Actuaries 2016 

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