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A Selective Review on Statistical Techniques for Big Data

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Modern Statistical Methods for Health Research

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

Abstract

To meet the big data challenges, many new statistical tools have been developed in recent years. In this review, we summarize some of these approaches to give an overview of the current state of the development. We will focus on the case that the number of observations is much larger than the dimension of the unknown parameters, although we will mention some investigations related to the high-dimensional data. We will discuss methods using subsamples as well as methods processing the whole data piece by piece.

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Authors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Yaqiong Yao & HaiYing Wang

Authors
  1. Yaqiong Yao
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  2. HaiYing Wang
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Correspondence to HaiYing Wang .

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Editors and Affiliations

  1. Department of Mathematics & Statistics, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  2. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, KwaZulu-Natal, South Africa

    (Din) Ding-Geng Chen

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Yao, Y., Wang, H. (2021). A Selective Review on Statistical Techniques for Big Data. In: Zhao, Y., Chen, (.DG. (eds) Modern Statistical Methods for Health Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-72437-5_11

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