巨灾死亡人数幂律分布与金融时间序列风险分析分析

巨灾死亡人数幂律分布与金融时间序列风险分析分析

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时间:2019-03-04

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1、浙江理工大学硕士学位论文AbstractInrecentyears,naturaldisastershappenedfrequentlywhichcausedthecasualtiesandpropertyloss,especiallytheearthquake.Thus,asonekindofextremeevens,earthquakeproblemswerefollowedbymanyresearchers.Inordertoefficientlyreducethelossescausedbyearthqu

2、akes,thisarticlemainlyfocusonapointthathowtousethepower-lawdistributiontoanalyzeanddiscusstherelationbetweendeathandfrequencywhichcausedbyearthquakes.Itispossibletoconfirmthatthedeathobeythetwo-stagepower-lawdistributionbydatacollectionandresultanalysis.Moreo

3、ver,itismuchbetterandaccuratetousethetwo-stagepower-lawdistributiontofittherelationbetweenthefrequencyanddeath.Then,thisarticlecananalyzeanddiscussthecorrelationoftwo-stagepower-lawdistribution,therelationamongscalingexponents,theinfluenceofscalingexponentsca

4、usedbytimesuchasaparticularyearormonthandtherelationbetweenthegeographicaldistributionofnationanddeath.Itispossibletoforecastthetrendofdeathsdevelopingaccordingtothescalingexponents.Inthissection,theresultswereshownasfollow:(1)Thefrequencyofearthquakeanddeath

5、sobeyedthetwo-stagepower-lawdistribution.Thefrequencyoftheearthquakethatcausedthemassivedeathswilldropquicklywithagreatriseofdeaths.Itmeansthatitisnoteasytohavemassivedeaths.Thus,whydothefrequency-deathsobeythefeatureoftwo-stagepower-lawdistribution?Itshouldb

6、eexplainedthatwhenthedeathsarrivedaparticularrange,theprobabilityofterribleearthquakewoulddropmoresharplywiththeincreaseofdeaths,whichwouldnotmatchthefrequencycorrespondingtothelowdeaths.(2)Fromtheperspectiveoftime,thisarticlediscoveredthatthefrequency-deaths

7、obeythetwo-stagepower-lawdistributionbyyears.Forthetwo-stagepower-lawdistribution,itisnearforthetrendoftwoscalingexponentvaluesandcriticalvalueofdeaths,whichmeansthatthesetwopartsofpower-lawdistributionareinterrelated.Thereisaproportionrelationbetweenthesecon

8、dscalingexponentandthefirst.However,ifconsideraboutthemonth,itcanbefoundthatthescalingexponentsofseveralmonthssuchasJanuary,May,June,JulyandOctoberissmallerthanothers.Thesemonthsarelikely

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