Skewness-based projection pursuit: A computational approach
Skewness-based projection pursuit: A computational approach
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基于偏度的投影追踪：一种计算方法
DOI：
10.1016/j.csda.2017.11.001
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
N. Loperfido
中科院分区：
文献类型：
--
作者：
N. Loperfido
投影寻踪是一种多元统计技术，旨在通过最大化通常称为投影指数的兴趣度度量来找到有趣的低维数据投影。投影寻踪的广泛应用受到投影指数最大化所固有的计算困难的阻碍。这个问题是在基于偏度的投影寻踪的框架内解决的，专注于最高的第三标准化累积量的数据预测。首先，它是出于使用的第三个多元，标准化的时刻，启动最大化程序的右主导奇异向量。其次，它提出了一个迭代算法的偏度最大化，它依赖于一个三阶多项式在两个变量的解析易处理的最大化。基于模拟数据的目视检查和正式测试都清楚地表明，通过正态数据的线性投影可实现的最大偏度的渐近分布可能是偏态正态的。基于偏度的投影寻踪揭示数据结构的潜力与奥运会十项全能数据说明。
Projection pursuit is a multivariate statistical technique aimed at finding interesting low-dimensional data projections by maximizing a measure of interestingness commonly known as projection index. Widespread use of projection pursuit has been hampered by the computational difficulties inherent to the maximization of the projection index. The problem is addressed within the framework of skewness-based projection pursuit, focused on data projections with highest third standardized cumulants. First, it is motivated the use of the right dominant singular vector of the third multivariate, standardized moment to start the maximization procedure. Second, it is proposed an iterative algorithm for skewness maximization which relies on the analytically tractable maximization of a third-order polynomial in two variables. Both visual inspection and formal testing based on simulated data clearly suggest that the asymptotic distribution of the maximal skewness achievable by a linear projection of normal data might be skew-normal. The potential of skewness-based projection pursuit for uncovering data structures is illustrated with Olympic decathlon data.
似然比检验对于检测两种成分的正态混合物有何强大作用？
影响因子：
1.9
作者：
Mendell,NR;Finch,SJ;ThodeJr,HC
通讯作者：
ThodeJr,HC