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ВИДЕОТЕКА |
5-ая международная конференция по стохастическим методам 2020
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Minimum Variance and Minimum Kulback-Leibler Mean Estimation with a Known Quantile Musoni Wilson, Zhanna Zenkova |
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Аннотация: This work compares two mean estimators, MV and MKL, which incorporate information about a known quantile. MV minimizes variance and MKL minimizes Kulback-Leibler divergence. Both estimators are asymptotically equivalent and normally distributed but differ at finite sample sizes. Monte-Carlo simulation studies show that MV has higher mean squared error than MKL in the majority of simulated scenarios. Authors recommend using MKL when a quantile of an underlying distribution is known. |