Statistical Inference Using Maximum Likelihood Estimation and the Generalized Likelihood Ratio when the True Parameter is on the Boundary of the Parameter Space

The classic asymptotic properties of the maximum likelihood estimator and generalized likelihood ratio statistic do not hold when the true parameter is on the boundary of the parameter space. An inferential procedure based on an enlarged parameter space is shown to have the classical asymptotic properties. Some other competing procedures are also examined.