I am trying to better understand and explain maximum likelihood estimation. To explain the intuition of many ML aspects I find it easiest to explain them graphically, like for example the ML-based tests LR/Score/Wald:

graphic illustration of different ML based tests

My question: Is there a similar sketch to be drawn for the most common/simple (OPG, Hessian, Sandwich) estimators for standard errors/confidence bands?

Idea: Intuitively I expect the uncertainty of my point estimate to be captured by the degree of convexity around the max. This fits with the definitions of some of the estimators. But I can't come up with a good way of drawing this

  • $\begingroup$ Good question. I have added a data-visualization tag though it does not fit perfectly (so feel free to remove it). $\endgroup$ – Richard Hardy Sep 12 '18 at 11:58

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