Sufficiency Revisited: Rethinking Statistical Algorithms in the Big Data Era-Reference-Cited by-同舟云学术

Sufficiency Revisited: Rethinking Statistical Algorithms in the Big Data Era

Published:2017-07-03 Issue:3 Volume:71 Page:202-208
ISSN:0003-1305
Container-title:The American Statistician
language:en
Short-container-title:The American Statistician

Author:

Lee Jarod Y. L.¹²,Brown James J.¹²,Ryan Louise M.¹²³

Affiliation:

1. School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW, Australia

2. Australian Research Council Centre of Excellence for Mathematical & Statistical Frontiers, The University of Melbourne, Parkville, VIC, Australia

3. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00031305.2016.1255659

Reference38 articles.

1. Australian Bureau of Statistics (2008), “Information Paper: An Introduction to Socio-Economic Indexes for Areas (SEIFA),” ABS Catalogue No. 2039.0, Australian Bureau of Statistics.

2. ——— (2012), “Australian Statistical Geography Standard (ASGS): Correspondences,” ABS Catalogue No. 1270.0.55.006, Australian Bureau of Statistics.

3. Symbolic Data Analysis

4. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

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