In many research articles the performance of an estimation method is compared to that of the ML estimation performance. If the performance of the method does not achieve the ML estimation performance, then the method is 'suboptimal' or not good enough. I don't quite remember what is the meaning of ML estimation performance and sub-optimal or optimal. When is a method optimal and optimal with respect to what? I am following the book titled: "Statistical signal processing Vol 1 by S. Kay". As a beginner and self-learner it is quite hard to grasp the actual implication of the terms -- achieving ML estimation performance, optimal and sub-optimal.
Can somebody please explain an intuitive meaning to these terms? Thank you.