偏正态分布下估计中位数的先验程序 (APP) 及其在经济学和金融学中的应用

Liqun Hu, Tonghui Wang, D. Trafimow, S. T. B. Choy, Xiangfei Chen, Cong Wang, Tingting Tong
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引用次数: 0

摘要

作者的结论是基于计算机模拟支持的数学推导和三个在经济和金融应用中的工作实例。最后,作者提供了一个计算机程序的链接,以便研究人员可以轻松地进行分析。设计/方法/方法基于参数估计目标,目前的工作是确定研究人员应该收集的最小样本量,以便他们的样本中位数可以被信任为相应总体中位数的良好估计。作者分别用正态近似法和精确法推导出两个解。结果:精确法比正态近似法提供更准确的答案。作者表明,使用精确方法估计中位数所需的最小样本量大大小于使用正态近似方法。因此,研究人员可以使用精确的方法来节省样本量。原创性/价值本文将先验方法推广到估计偏态条件下的总体中值。数学推导和计算机模拟使用样本中位数来估计总体中位数的精确方法是新的,并提供了一个免费和用户友好的计算机程序的链接,以便研究人员可以自己进行计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The a priori procedure (APP) for estimating median under skew normal settings with applications in economics and finance
PurposeThe authors’ conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance. Finally, the authors provide a link to a computer program so that researchers can perform the analyses easily.Design/methodology/approachBased on a parameter estimation goal, the present work is concerned with determining the minimum sample size researchers should collect so their sample medians can be trusted as good estimates of corresponding population medians. The authors derive two solutions, using a normal approximation and an exact method.FindingsThe exact method provides more accurate answers than the normal approximation method. The authors show that the minimum sample size necessary for estimating the median using the exact method is substantially smaller than that using the normal approximation method. Therefore, researchers can use the exact method to enjoy a sample size savings.Originality/valueIn this paper, the a priori procedure is extended for estimating the population median under the skew normal settings. The mathematical derivation and with computer simulations of the exact method by using sample median to estimate the population median is new and a link to a free and user-friendly computer program is provided so researchers can make their own calculations.
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