Kernel density estimation for length biased data
Kernel density estimation for length biased data
复制标题
长度偏差数据的核密度估计
DOI：
10.1093/biomet/78.3.511
复制
发表时间：
1991
期刊：
影响因子：
2.7
通讯作者：
M. C. Jones
中科院分区：
文献类型：
--
作者：
M. C. Jones
本文提出并研究了一种新的长度有偏数据核密度估计，它是由非参数极大似然估计的光滑化而得。与Bhattacharyya，富兰克林和Richardson（1988）提出的另一种方法相比，它具有各种优点：它必然是概率密度，在零附近表现得特别好，它具有更好的渐近均方误差性质，并且它更容易扩展到相关问题，如密度导数估计。
A new kernel density estimator for length biased data which derives from smoothing the nonparametric maximum likelihood estimator is proposed and investigated. It has various advantages over an alternative method suggested by Bhattacharyya, Franklin & Richardson (1988): it is necessarily a probability density, it is particularly better behaved near zero, it has better asymptotic mean integrated squared error properties and it is more readily extendable to related problems such as density derivative estimation.