Discriminant analysis through a semiparametric model
Discriminant analysis through a semiparametric model
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DOI：
10.1093/biomet/90.2.379
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发表时间：
2003-06-01
期刊：
影响因子：
2.7
通讯作者：
Jeon, Y
中科院分区：
文献类型：
--
作者：
Lin, Y;Jeon, Y
我们考虑正态理论判别分析的半参数推广。半参数模型假设，在未指定的单变量单调变换之后，类分布是多元正态分布。我们引入了一种基于分布分位数的估计过程，其中直接估计血清参数模型的参数，而不估计非参数变换。该过程计算速度快，并且估计精度具有通常的参数率。讨论了该方法与更一般的非参数判别分析之间的关系。类密度的半参数指定是非参数对数密度函数方差分析模型的子模型，其中主效应完全是非参数的，但交互项是半参数指定的。使用模拟和真实示例来说明该过程。
We consider a semiparametric generalisation of normal-theory discriminant analysis. The semiparametric model assumes that, after unspecified univariate monotone transformations, the class distributions are multivariate normal. We introduce an estimation procedure based on the distribution quantiles, in which the parameters of the sermparametric model are estimated directly without estimating the nonparametric transformations. The procedure is computationally fast and the estimation accuracy is shown to have the usual parametric rate. The relationship between the method and more general nonparametric discriminant analysis is discussed. The semiparametric specification of the class densities is a submodel of the nonparametric log density functional analysis of variance model in which the main effects are completely nonparametric but the interaction terms are specified semiparametrically. Simulations and real examples are used to illustrate the procedure.