TY - JOUR
T1 - A Nested Copula Duration Model for Competing Risks with Multiple Spells
AU - Lo, Simon M. S.
AU - Mammen, Enno
AU - Wilke, Ralf
N1 - Published online: 25 April 2020
PY - 2020/10
Y1 - 2020/10
N2 - A copula graphic estimator for the competing risks duration model with multiple spells is presented. By adopting a nested copula structure the dependencies between risks and spells are modelled separately. This breaks up an implicit restriction of popular duration models such as multivariate mixed proportional hazards. It is shown that the dependence structure between spells is identifiable and can be estimated, in contrast to the dependence structure between competing risks. Thus, by allowing these two components to differ, the model is not identifiable. This is an important finding related to the general identifiability of competing risks models. Various features of the model are investigated by simulations and its practicality is illustrated by an application to unemployment duration data.
AB - A copula graphic estimator for the competing risks duration model with multiple spells is presented. By adopting a nested copula structure the dependencies between risks and spells are modelled separately. This breaks up an implicit restriction of popular duration models such as multivariate mixed proportional hazards. It is shown that the dependence structure between spells is identifiable and can be estimated, in contrast to the dependence structure between competing risks. Thus, by allowing these two components to differ, the model is not identifiable. This is an important finding related to the general identifiability of competing risks models. Various features of the model are investigated by simulations and its practicality is illustrated by an application to unemployment duration data.
KW - Nested archimedean coupla
KW - Multiple occurrences
KW - Frailty
KW - Nested archimedean coupla
KW - Multiple occurrences
KW - Frailty
UR - https://sfx-45cbs.hosted.exlibrisgroup.com/45cbs?url_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&ctx_enc=info:ofi/enc:UTF-8&ctx_ver=Z39.88-2004&rfr_id=info:sid/sfxit.com:azlist&sfx.ignore_date_threshold=1&rft.object_id=954926232411
U2 - 10.1016/j.csda.2020.106986
DO - 10.1016/j.csda.2020.106986
M3 - Journal article
VL - 150
JO - Computational Statistics & Data Analysis
JF - Computational Statistics & Data Analysis
SN - 0167-9473
M1 - 106986
ER -