Abstract

The role of quantile functions in modeling various forms of statistical data is well established. Generally classical procedures like method of moments, L-moments, percentiles etc are employed in estimating the parameters of the model. In the present work an attempt is made to infer parameters in the Bayesian framework with special emphasis to distributions in which the quantile functions do not posses tractable distribution functions. The procedure is illustrated for some distributions and real life data.

中文翻译：

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分位数函数中的贝叶斯推理

摘要

分位数函数在对各种形式的统计数据建模中的作用已经确立。通常采用经典程序，如矩法、L矩、百分位数等来估计模型的参数。在目前的工作中，尝试在贝叶斯框架中推断参数，特别强调分位数函数不具有易处理分布函数的分布。该过程针对一些分布和现实生活数据进行了说明。