EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION. Issue 3 (13th May 2016)

• Record Type: Journal Article
• Authors: Yin, Cuihong; Lin, X. Sheldon
• Is Part Of: ASTIN bulletin. Volume 46:Issue 3(2016)
• Page Start: 779
• Page End: 799
• Publication Date: 2016-05-13
• Subjects: Erlang mixture, -- EM algorithm, -- iSCAD penalty, -- VaR, -- TVaR; Insurance -- Mathematics -- Periodicals; Risk (Insurance) -- Mathematics -- Periodicals; 368.01
• DOI: 10.1017/asb.2016.14
• Languages: English
• ISSNs: 0515-0361

Abstract

Abstract: The Erlang mixture model has been widely used in modeling insurance losses due to its desirable distributional properties. In this paper, we consider the problem of efficient estimation of the Erlang mixture model. We present a new thresholding penalty function and a corresponding EM algorithm to estimate model parameters and to determine the order of the mixture. Using simulation studies and a real data application, we demonstrate the efficiency of the EM algorithm.