UofT ActSci
UofT ActSci
Home
People
Publications
Software
Event
Light
Dark
Automatic
Insurance loss modeling
On the consistency of penalized MLEs for Erlang mixtures
In Yin and Lin (2016), a new penalty, termed as iSCAD penalty, is proposed to obtain the maximum likelihood estimates (MLEs) of the weights and the common scale parameter of an Erlang mixture model. In that paper, it is shown through simulation …
Cite
×