英文标题：
《Box-Cox transformation of firm size data in statistical analysis》
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作者：
Ting Ting Chen, Tetsuya Takaishi
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最新提交年份：
2015
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英文摘要：
Firm size data usually do not show the normality that is often assumed in statistical analysis such as regression analysis. In this study we focus on two firm size data: the number of employees and sale. Those data deviate considerably from a normal distribution. To improve the normality of those data we transform them by the Box-Cox transformation with appropriate parameters. The Box-Cox transformation parameters are determined so that the transformed data best show the kurtosis of a normal distribution. It is found that the two firm size data transformed by the Box-Cox transformation show strong linearity. This indicates that the number of employees and sale have the similar property as a firm size indicator. The Box-Cox parameters obtained for the firm size data are found to be very close to zero. In this case the Box-Cox transformations are approximately a log-transformation. This suggests that the firm size data we used are approximately log-normal distributions.
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中文摘要：
企业规模数据通常不显示回归分析等统计分析中经常假设的正态性。在本研究中，我们关注两个公司规模数据：员工数量和销售额。这些数据大大偏离了正态分布。为了提高这些数据的正态性，我们使用具有适当参数的Box-Cox变换对它们进行变换。确定Box-Cox变换参数，以便变换后的数据最好地显示正态分布的峰度。研究发现，通过Box-Cox变换得到的两个企业规模数据具有很强的线性关系。这表明员工数量和销售额与公司规模指标具有相似的属性。公司规模数据的Box-Cox参数非常接近于零。在这种情况下，Box-Cox变换近似于对数变换。这表明我们使用的公司规模数据近似于对数正态分布。
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分类信息：

一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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