Kernel Density Estimation for Length Biased Data
提出一种新的核密度估计量，通过对非参数最大似然估计进行平滑处理来应对长度偏差数据，相比已有方法在密度性质、近零表现和渐近误差方面更优。
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.