楼主发论文资料时可以贴一下论文的摘要，这样更清楚一点

Many insurance loss data are known to be heavy-tailed. In this article we study the class of Log
phase-type (LogPH) distributions as a parametric alternative in fitting heavy tailed data. Transformed
from the popular phase-type distribution class, the LogPH introduced by Ramaswami exhibits several
advantages over other parametric alternatives. We analytically derive its tail related quantities including
the conditional tail moments and the mean excess function, and also discuss its tail thickness in the
context of extreme value theory. Because of its denseness proved herein, we argue that the LogPH can
offer a rich class of heavy-tailed loss distributions without separate modeling for the tail side, which is
the case for the generalized Pareto distribution (GPD). As a numerical example we use the well-known
Danish fire data to calibrate the LogPH model and compare the result with that of the GPD.Wealso present
fitting results for a set of insurance guarantee loss data.

摘要: Many insurance loss data are known to be heavy-tailed. In this article we study the class of Log
phase-type (LogPH) distributions as a parametric alternative in fitting heavy tailed data. Transformed
from the popular phase-type distribution class, the LogPH introduced by Ramaswami exhibits several
advantages over other parametric alternatives. We analytically derive its tail related quantities including
the conditional tail moments and the mean excess function, and also discuss its tail thickness in the
context of extreme value theory. Because of its denseness proved herein, we argue that the LogPH can
offer a rich class of heavy-tailed loss distributions without separate modeling for the tail side, which is
the case for the generalized Pareto distribution (GPD). As a numerical example we use the well-known
Danish fire data to calibrate the LogPH model and compare the result with that of the GPD.Wealso present
fitting results for a set of insurance guarantee loss data.