Statistical Science

Statistical Theory Powering Data Science

Junhui Cai, Avishai Mandelbaum, Chaitra H. Nagaraja, Haipeng Shen, and Linda Zhao

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Abstract

Statisticians are finding their place in the emerging field of data science. However, many issues considered “new” in data science have long histories in statistics. Examples of using statistical thinking are illustrated, which range from exploratory data analysis to measuring uncertainty to accommodating nonrandom samples. These examples are then applied to service networks, baseball predictions and official statistics.

Article information

Source
Statist. Sci., Volume 34, Number 4 (2019), 669-691.

Dates
First available in Project Euclid: 8 January 2020

Permanent link to this document
https://projecteuclid.org/euclid.ss/1578474031

Digital Object Identifier
doi:10.1214/19-STS754

Mathematical Reviews number (MathSciNet)
MR4048597

Keywords
Service networks queueing theory empirical Bayes nonparametric estimation sports statistics decennial census house price index

Citation

Cai, Junhui; Mandelbaum, Avishai; Nagaraja, Chaitra H.; Shen, Haipeng; Zhao, Linda. Statistical Theory Powering Data Science. Statist. Sci. 34 (2019), no. 4, 669--691. doi:10.1214/19-STS754. https://projecteuclid.org/euclid.ss/1578474031


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