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	Direct regression modelling of high-order moments in big data
	Xi, Ruibin ; Lin, Nan
	2016
关键词	Big data Higher-order moment U-statistics Estimating equation Divide-and-conquer Aggregation Consistency Asymptotic normality Data cube COPY NUMBER VARIATION AGGREGATION COMPRESSION CUBES
英文摘要	Big data problems present great challenges to statistical analyses, especially from the computational side. In this paper, we consider regression estimation of high-order moments in big data problems based on the U-statistic-based Functional Regression Model (U-FRM) model. The U-FRM model is a nonparametric method that allows direct estimation of higher-order moments without imposing parametric assumptions on the high order-moments. Despite this modeling advantage, its estimation relies on a U-statistics based estimating equation whose computational complexity is generally too high for big data. In this paper, we propose using the "divide-and-conquer" strategy to construct a computationally more succinct surrogate estimating equation. Through both theoretical proof and simulations, we show that our method significantly reduces the computational time and meanwhile enjoys the same asymptotic behavior as the original estimation method. We then apply our method to a genomic problem to illustrate its performance on real data.; CPCI-S(ISTP); 4; 445-452; 9
语种	英语
出处	SCI
出版者	Banff Workshop on Statistical and Computational Theory and Methodology for Big Data Analysis
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/460185]
专题	数学科学学院
推荐引用方式 GB/T 7714	Xi, Ruibin,Lin, Nan. Direct regression modelling of high-order moments in big data. 2016-01-01.

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