In the present communication, we introduce quantile-based (dynamic) inaccuracy measures and study their properties. Such measures provide an alternative approach to evaluate inaccuracy contained in the assumed statistical models. There are several models for which quantile functions are available in tractable form, though their distribution functions are not available in explicit form. In such cases, the traditional distribution function approach fails to compute inaccuracy between two random variables. Various examples are provided for illustration purpose. Some bounds are obtained. Effect of monotone transformations and characterizations are provided.
机构:
Cornell Univ, Dept Stat & Data Sci, Ithaca, NY 14853 USACornell Univ, Dept Stat & Data Sci, Ithaca, NY 14853 USA
Zhang, Tao
Kato, Kengo
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Cornell Univ, Dept Stat & Data Sci, Ithaca, NY 14853 USACornell Univ, Dept Stat & Data Sci, Ithaca, NY 14853 USA
Kato, Kengo
Ruppert, David
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Cornell Univ, Dept Stat & Data Sci, Ithaca, NY 14853 USA
Cornell Univ, Sch Operat Res & Informat Engn, Ithaca, NY USACornell Univ, Dept Stat & Data Sci, Ithaca, NY 14853 USA