bold upper M bold q"/> and obtain it in time logarithmic in 5 Rise of Data Science
Core Challenges 4 and 5 – fast, flexible, and user‐friendly algo‐ware and hardware‐optimized inference – embody an increasing emphasis on application and implementation in the age of data science. Previously undervalued contributions in statistical computing, for example, hardware utilization, database methodology, computer graphics, statistical software engineering, and the human–computer interface [76], are slowly taking on greater importance within the (rather conservative) discipline of statistics. There is perhaps no better illustration of this trend than Dr. Hadley Wickham's winning the prestigious COPSS Presidents' Award for 2019
[for] influential work in statistical computing, visualization, graphics, and data analysis; for developing and implementing an impressively comprehensive computational infrastructure for data analysis through R software; for making statistical thinking and computing accessible to large audience; and for enhancing an appreciation for the important role of statistics among data scientists [106].
This success is all the more impressive because Presidents' Awardees have historically been contributors to statistical theory and methodology, not Dr. Wickham's scientific software development for data manipulation [107–109] and visualization [110, 111].
All of this might lead one to ask: does the success of data science portend the declining significance of computational statistics and its Core Challenges? Not at all! At the most basic level, data science's emphasis on application and implementation underscores the need for computational thinking in statistics. Moreover, the scientific breadth of data science brings new applications and models to the attention of statisticians, and these models may require or inspire novel algorithmic techniques. Indeed, we look forward to a golden age of computational statistics, in which statisticians labor within the intersections of mathematics, parallel computing, database methodologies, and software engineering with impact on the entirety of the applied sciences. After all, significant progress toward conquering the Core Challenges of computational statistics requires that we use every tool at our collective disposal.
Acknowledgments
AJH is supported by NIH grant K25AI153816. MAS is supported by NIH grant U19AI135995 and NSF grant DMS1264153.
Notes
1 1 Statistical inference is an umbrella term for hypothesis testing, point estimation, and the generation of (confidence or credible) intervals for population functionals (mean, median, correlations, etc.) or model parameters.
2 2 We present the problem of phylogenetic reconstruction in Section 3.2 as one such example arising from the field of molecular epidemiology.
3 3 The use of “N” and “P” to denote observation and parameter count is common. We have taken liberties in coining the use of “M” to denote mode count.
4 4 A more numerically stable approach has the same complexity [24].
5 5 The matrix parameter coincides with for linear regression and for auxiliary Pólya‐Gamma parameter for logistic regression [56, 57].
6 6 See Nishimura and Suchard [57] and references therein for the role and design of a preconditioner.
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