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.
No takes yet. Share an insight, caveat, or question.
Cordy et al. (1997) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: