Robust test statistics induced from the minimum L2 distance estimation

The test statistics are derived based on a minimum problem of L-2 distance between the model density and a corresponding density estimator when the null hypothesis is simple or composite involving subspace. The newly proposed tests are asymptotically efficient and robust against slight contamination on an underlying density. The proposed test statistics are easy enough to be handled without extensive knowledge of computing unlike some of the existing deviance tests.