分析大规模计数数据的 A-最优子抽样方法

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Nonparametric Statistics Pub Date : 2024-07-30 DOI:10.1080/10485252.2024.2383307

Fei Tan, Xiaofeng Zhao, Hanxiang Peng

引用次数: 0

摘要

在开发随机算法和分析海量数据时，经常会用到均匀分布和基于统计杠杆分数的（非均匀）分布。这两种分布都是随机的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The A-optimal subsampling approach to the analysis of count data of massive size

The uniform and the statistical leverage-scores-based (nonuniform) distributions are often used in the development of randomised algorithms and the analysis of data of massive size. Both distributi...

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来源期刊

Journal of Nonparametric Statistics 数学-统计学与概率论

CiteScore

1.50

自引率

8.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modeling, Nonparametric function estimation, Rank and other robust and distribution-free procedures, Resampling methods, Lack-of-fit testing, Multivariate analysis, Inference with high-dimensional data, Dimension reduction and variable selection, Methods for errors in variables, missing, censored, and other incomplete data structures, Inference of stochastic processes, Sample surveys, Time series analysis, Longitudinal and functional data analysis, Nonparametric Bayes methods and decision procedures, Semiparametric models and procedures, Statistical methods for imaging and tomography, Statistical inverse problems, Financial statistics and econometrics, Bioinformatics and comparative genomics, Statistical algorithms and machine learning. Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.