Suboptimal is best (Michael Lavine - ARO)

Many statistics problems are framed as optimization. That is, we write down a target function f(θ) and find the input value θ* that maximizes it. Our thesis is that we are often better served by finding the entire set of θ's that come close to maximizing f viz; Θ* ={θ : f(θ) ≥ f(θ*) - ε }.

This talk will

1. Explain why (briefly);

2. Show a few examples of what can be gained by finding Θ* and

3. Show one possible approach to finding Θ*.