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Penalized maximum likelihood estimates
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 …
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