Deconvolution of a Distribution Function
研究了当观测数据受测量误差污染时如何估计分布函数，将未知分布建模为有限个已知分布的混合，并利用似然理论估计参数和构造置信区间，适用于单峰分布，通过膳食调查数据展示应用。
Abstract We consider the estimation of a distribution function when observations from this distribution are contaminated by measurement error. The unknown distribution is modeled as a mixture of a finite number of known distributions. Model parameters can be estimated and confidence intervals constructed using well-known likelihood theory. We show that it is also possible to apply this approach to estimation of a unimodal distribution. An application is presented using data from a dietary survey. Simulation results are given to indicate the performance of the estimators and the confidence interval procedures.