Statistical Modelling for Big and Little Data

被引:0
|
作者
Henderson, Robin [1 ]
机构
[1] Newcastle Univ, Newcastle Upon Tyne, Tyne & Wear, England
来源
DEVELOPMENTS IN STATISTICAL MODELLING, IWSM 2024 | 2024年
关键词
Data science; Extrapolation; Inference; Smoothing; Two cultures;
D O I
10.1007/978-3-031-65723-8_38
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
While the difference between "Data Science" and "Statistics" disciplines is, at best, blurred, many people associate machine learning methods and big data with the former, and modelling and inference for small samples (little data) with the latter. We present a big data application where no sophisticated method at all is needed, a small data application where a partial modelling approach seems useful, and a big-and-little data application where we can borrow strength from limited information in a large sample, to improve estimation based on more detailed data in a small sample.
引用
收藏
页码:246 / 254
页数:9
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