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基于经验分布与空气之间的距离三级数据应用统计推断
Statistical Inference Based on Distances Between Empirical Distributions with Applications to AIRS Level-3 Data |
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| 课程网址: |
http://videolectures.net/cidu2011_shi_airs/
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| 主讲教师: |
Tao Shi
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| 开课单位: |
俄亥俄州立大学
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| 开课时间: |
2012-06-27
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| 课程语种: |
英语
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| 中文简介: |
美国宇航局 aqua 卫星上的传感器--大气红外发声仪 (airs) 一直在收集温度、水汽质量混合比、不同大气压力水平的云分数和其他大气观测。airs 2级数据的地面足迹为45公里, 覆盖全球。airs 3级量化 (l3q) 产品通过一组具有代表性的向量及其相关权重, 在一段时间内总结出每个 5o × 5o 5 纬度经度网格框中的有效2级数据, 这些向量及其相关权重可视为经验分布。在本文中, 我们研究了潜在的统计工具使用成对的差异, 适合于分析这种非传统类型的数据。通过理论分析和仿真, 我们研究了几种不同的度量方法, 发现当代表性向量的位置对分析很重要时, m允许距离比其他方法更可取。我们应用多维量尺度和聚类方法来分析2002年12月收集的 airs 数据。这些研究的结果提供了一个洞察, 如何基于 m允许距离的统计方法可以从 airs l3q 数据中提取更多的信息, 而不是从每个网格框中的简单样本平均摘要。
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| 课程简介: |
Atmospheric Infrared Sounder (AIRS), a sensor aboard NASA’s Aqua satellite, has been collect- ing temperatures, water vapor mass-mixing ratios, cloud fractions at various atmosphere pressure levels, and other atmospheric observations. AIRS level 2 data has a 45 km ground footprint with global coverage. The AIRS level 3 Quantization (L3Q) product summarizes valid level 2 data in each 5o × 5o latitude-longitude grid box during a time period by a set of representative vectors and their associated weights, which can be treated as an empirical distribution. In this paper, we study potential statistical tools using pairwise dissimilarities that are suitable for analyzing this nontraditional type of data. Through theoretical analysis and simulations, we investigate several different dissimilarity measures and find Mallows distance is preferable over others when the locations of the representative vectors are important for the analysis. We apply MultiDimensional Scaling and clustering method to analyze AIRS data collected in December 2002. The results from these studies provide insights on how statistical methods based on Mallows distance may extract more information from the AIRS L3Q data than from the simple sample average summary in each grid box.
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| 关 键 词: |
计算机科学; 数据挖掘; 统计和共识的方法
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-04：毛岱琦(课程编辑志愿者)
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| 阅读次数: |
37
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