On the Non-Parametric Estimation of Mixture Proportions
研究了基于核密度估计的混合比例非参数估计在数据截断或舍入时的缺陷，提出基于经验分布函数的估计量，其效率随组分分布距离增大而接近100%。
SUMMARY Non-parametric estimates of mixing proportions based on kernel-type density estimators are badly suited to several types of data. They suffer from aberrations due to rounding or truncation of the measurements, and their construction involves the crucial choice of the “window size”, or smoothing parameter. In many circumstances estimators based on the empiric distribution function would be more suitable, and in this paper we investigate their properties. The estimators we introduce lead in a natural way to non-parametric forms of well-known parametric estimators. Their efficiency approaches 100 per cent as the distances between the component distributions increase.