Approximation of unknown distribution functions has been an important problem in many statistical models. The article addresses the problem using Edgeworth expansions with random weights. This method is an alternative to the bootstrap and may be applied to nonidentically distributed variables. Imposing some moment conditions, the article proves approximation results with accuracy $n^{-1}\sqrt{\ln\ln n}$, which improve earlier results.