Statistical inference of stochastic optimization problems
نویسنده
چکیده
We discuss in this paper asymptotic statistical inference of stochastic optimization problems. These are optimization problems where the “true” objective function, and probably some of the constraints, are estimated, typically by averaging a random sample. The classical maximum likelihood estimation can be considered in that framework. Recently statistical analysis of such problems has been motivated by a development of simulation based optimization. We investigate asymptotic properties of the optimal value and an optimal solution of such stochastic problems by employing the so-called delta method, and discuss some examples.
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