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dc.contributor.advisor黄荣坦
dc.contributor.author张鑫梅
dc.date.accessioned2018-12-05T01:40:22Z
dc.date.available2018-12-05T01:40:22Z
dc.date.issued2017-12-27
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/170044
dc.description.abstract在统计学中,如何较好地拟合一组给定数据的密度函数并给出密度曲线的参数形式一直备受关注。尤其是实际问题中经常遇见的删失数据和多峰数据的拟合,不但要求密度曲线具有高度的灵活性,而且不能出现过度拟合现象,这就大大增加了拟合难度。其中,对于混合模型的研究越来越受到人们的重视,是因为混合模型是一种介于参数方法与非参数方法间的半参数模型,这种半参数模型的优越在于它既避免了参数模型对数据结构的拟合偏离问题又具有分布函数可知等非参数模型无法具备的拟合性质。继Tijims[2]给出证明,在弱收敛的意义下,具有相同尺度参数的混合Erlang模型可以无限逼近任意分布后,利用混合Erlang模型解决金融、保险等行业的...
dc.description.abstractIn statistics, the problem of how to give the density function of given data well by mathematical expression has drawn too much attention. Especially when it comes to the censored data or multi-mode data, the density curve needs not only be of high flexibility but also can get rid of overfit, which greatly increase the difficulty of fitting.Among them, the development of mixed model has received p...
dc.language.isozh_CN
dc.relation.urihttps://catalog.xmu.edu.cn/opac/openlink.php?strText=58241&doctype=ALL&strSearchType=callno
dc.source.urihttps://etd.xmu.edu.cn/detail.asp?serial=60807
dc.subject混合Erlang模型
dc.subjectEM算法
dc.subject惩罚似然估计
dc.subjectMixed Erlang
dc.subjectEM algorithm
dc.subjectPenalized Likelihood Estimations
dc.title粗糙惩罚混合Erlang模型在密度估计上的应用
dc.title.alternativeFitting mixed Erlang densities under Roughness Penalty
dc.typethesis
dc.date.replied2017-05-23
dc.description.note学位:理学硕士
dc.description.note院系专业:数学科学学院_概率论与数理统计
dc.description.note学号:19020141152622


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