Kernel density estimation for directional-linear data
Kernel density estimation for directional-linear data
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DOI：
10.1016/j.jmva.2013.06.009
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
E. García-Portugués;R. Crujeiras;W. González-Manteiga
中科院分区：
文献类型：
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作者：
E. García-Portugués;R. Crujeiras;W. González-Manteiga
提出了一种方向线性数据的非参数核密度估计。该建议是基于一个产品的内核占两个（方向和线性）组件的随机向量的不同性质。表达式的偏差，方差，和平均积分平方误差（MISE）的推导，联合与渐近正态性结果的估计。对于某些特定的分布，MISE的一个明确的公式，并与其渐近版本相比，无论是方向和方向线性核密度估计。在此相同的设置，封闭的表达式的引导MISE也来自。
A nonparametric kernel density estimator for directional–linear data is introduced. The proposal is based on a product kernel accounting for the different nature of both (directional and linear) components of the random vector. Expressions for the bias, variance, and mean integrated square error (MISE) are derived, jointly with an asymptotic normality result for the proposed estimator. For some particular distributions, an explicit formula for the MISE is obtained and compared with its asymptotic version, both for directional and directional–linear kernel density estimators. In this same setting, a closed expression for the bootstrap MISE is also derived.