Nonparametric Estimates Of Mixture Distribution And Bootstrap Confidence Interval For Deconvolution Models

There are many unobservable variables in some practical fields, For this, de-convolution and mixture distribution has been developed and most widely used. In this paper, we consider the estimation of a distribution function when observations from this distribution are contaminated by measurement error. The approach for using mixture distributions and bootstrap simulations is used to solve this problem, For two parts, distribution function and confidence interval, we show that our result is much better than Clifford, B. C.