01632nas a2200157   4500008004100000245005800041210005800099260000900157300001400166490000600180520113000186653001701316100002401333700002101357856009601378       2009                        eng d00aExtreme Value Analysis for Partitioned Insurance Loss0 aExtreme Value Analysis for Partitioned Insurance Loss  c2009  a214 - 2380 v33 aThe heavy-tailed nature of insurance claims requires that special attention be put into the analysis of the tail behavior of a loss distribution. It has been demonstrated that the distribution of large claims of several lines of insurance have Pareto-type tails. As a result, estimating the tail index, which is a measure of the heavy-tailedness of a distribution, has received a great deal of attention. Although numerous tail index estimators have been proposed in the literature, many of them require detailed knowledge of individual losses and are thus inappropriate for insurance data in partitioned form. In this study we bridge this gap by developing a tail index estimator suitable for partitioned loss data. This estimator is robust in the sense that no particular global density is assumed for the loss distribution. Instead we focus only on fitting the model in the tail of the distribution where it is believed that the Pareto-type form holds. Strengths and weaknesses of the proposed estimator are explored through simulation and an application of the estimator to real world partitioned insurance data is given.10aSupply Chain1 aIII, John, B. Henry1 aHsieh, Ping-Hung  u/biblio/extreme-value-analysis-partitioned-insurance-loss-000473nas a2200121   4500008004100000245005900041210005900100260003300159653001700192100002100209700002400230856009700254       2005                        eng d00aTail Index Estimation for Partitioned Insurance Losses0 aTail Index Estimation for Partitioned Insurance Losses  aMinneapolis, Minnesotac200510aSupply Chain1 aHsieh, Ping-Hung1 aIII, John, B. Henry  u/biblio/tail-index-estimation-partitioned-insurance-losses-0