Quantile Optimization for Heavy-tailed Distributions Using Asymmetric Signum Functions
نویسندگان
چکیده
In this paper, we present a provably convergent algorithm for computing the quantile of a random variable that does not require storing all of the sample realizations. We then present an algorithm for optimizing the quantile of a random function which may be characterized by a heavy-tailed distribution where the expectation is not defined. The algorithm is illustrated in the context of electricity trading in the presence of storage, where electricity prices are known to be heavy-tailed with infinite variance.
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تاریخ انتشار 2011