| 陈震,陆松,李国辉,张和平.安徽省火灾经济损失的尾部分布研究[J].火灾科学,2013,22(3):161-166. |
| 安徽省火灾经济损失的尾部分布研究 |
| Tail distribution of fire loss data in Anhui province |
| 投稿时间：2013-04-01 修订日期：2013-06-16 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI：10.3969/j.issn.1004-5309.2013.03.08 |
| 基金项目:国家自然科学基金项目资助(91024027) |
|
| 中文关键词: 火灾 经济损失 幂律分布 统计分析 |
| 英文关键词:Fires Loss Power-law distribution Statistic analysis |
| 摘要点击次数: 1095 |
| 全文下载次数: 755 |
| 中文摘要: |
| 采用极大似然估计对安徽火灾经济损失进行了幂律分布拟合，采用Kolmogorov-Smirnov 统计判断拟合优度，并选择了4种供选分布作为对比，研究何种分布更适用于描述火灾经济损失数据。研究发现，当经济损失大于100万元时，数据明显偏离幂律分布，通过p值可以拒绝数据服从幂律分布的假设。在5种分布中指数截断幂律分布的拟合效果最好，通过指数截断能够描述数据末端偏离幂律行为的现象。放火和生产作业两类原因的火灾，不仅满足幂律分布，而且指数截断幂律分布的拟合效果最优；不明确原因和静电两类原因的火灾，经济损失仅满足幂律分布；其他7种火灾原因对应的损失数据不能通过幂律分布拟合的p值检验。 |
| 英文摘要: |
| The power-law distribution of fire loss statistics in Anhui province was estimated by means of Maximum likelihood estimators. The goodness-of-fit test of power-law estimation was conducted in terms of Kolmogorov-Smirnov statistics. In order to judge whether it is possible another distribution might give a good or better fit, four alternative distributions were chosen and analyzed. According to results, the data significantly diverges from power-law distribution when the loss is over thousand Yuan. As the p-value of goodness-of-fit is zero, the power-law distribution is not a plausible hypothesis for the data. The power-law with an exponential cutoff is clearly favored over the other four distributions, and it can depict the deviation from power-law distribution in the end of loss. For fires caused by arson and working, their loss data pass the p-value of goodness-of-fit test, and the power-law with an exponential cutoff is more favored than the power-law distributions. For fires caused by unknown and electrostatic, their loss data pass the p-value of goodness-of-fit test as well, and power-law is the best fitting. For fires caused by the other 7 causes, their loss data cannot pass the p-value of goodness-of-fit test. |
| 关闭 |
