Computational Statistics
18 Sep 2024 10:22
— I am drawing a somewhat arbitrary terminological divide between "statistical computing", meaning computing environments for statistical data analysis, and "computational statistics", meaning computational methods of special relevance to statistical problems, or tricky or interesting computational problems arising from statistical problems. (One might even call it "numerical methods for statistics", except that some of the most relevant algorithms aren't very numerical.) When I teach statistical computing, only part of what I teach is actually computational statistics...
- See also:
- Bootstrap
- Computation
- Cross-Validation
- Data Mining
- Indirect Inference
- Monte Carlo
- Optimization
- Programming
- Simulations
- Statistical Computing
- Statistics
- Stochastic Approximation
- Statistical Emulators for Simulation Models
- Recommended, bigger picture:
- Michael I. Jordan, "On statistics, computation and scalability", Bernoulli 19 (2013): 1378--1390
- John F. Monahan, Numerical Methods of Statistics
- Recommended, very miscellaneous close-ups:
- Venkat Chandrasekaran and Michael I. Jordan, "Computational and Statistical Tradeoffs via Convex Relaxation", Proceedings of the National Academy of Sciences (USA) 110 (2013): E1181--E1190, arxiv:1211.1073
- Guosheng Yin, Yanyuan Ma, Faming Liang, and Ying Yuan, "Stochastic Generalized Method of Moments", Journal of Computational and Graphical Statistics forthcoming (2011)
- To read:
- Stéphanie Allassonniere, Estelle Kuhn, "Convergent Stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation", arxiv:1207.5938
- Johannes Blömer, Kathrin Bujna, Daniel Kuntze, "A Theoretical and Experimental Comparison of the EM and SEM Algorithm", arxiv:1310.5034
- Luc Devroye, Non-Uniform Random Variate Generation
- James E. Gentle, Elements of Computational Statistics
- J. E. Gentle, W. Härdle, Y. Mori (eds.), Handbook of Computational Statistics
- Matt Hoffman, David M. Blei, Chong Wang and John Paisley, "Stochastic Variational Inference", arxiv:1206.7051
- Michael Kane, John W. Emerson, Stephen Weston, "Scalable Strategies for Computing with Massive Data", Journal of Statistical Software 55 (2013): 14
- Robert Mariano, Til Schuermann and Melyvn J. Weeks (eds.), Simulation-Based Inference in Econometrics: Methods and Applications
- Norman Matloff, "Software Alchemy: Turning Complex Statistical Computations into Embarrassingly-Parallel Ones", arxiv:1409.5827
- National Research Council
- Jeffrey S. Racine, "A Primer on Regression Splines" [PDF preprint]
- Rajesh Ranganath, Sean Gerrish, David Blei, "Black Box Variational Inference", AI Stats 2014
- Thomas J. Santner, Brian J. Williams and William J. Note, Design and Analysis of Computer Experiments
- Sonnenberg et al., "The SHOGUN Machine Learning Toolbox", Journal of Machine Learning Research 11 (2010): 1799--1802
- James C. Spall, Introduction to Stochastic Search and Optimization [Book website]
- Ronald A. Thisted, Elements of Statistical Computing
- Mitchell Watnik, "Early Computational Statistics", Journal of Computational and Graphical Statistics 20 (2011): 811--817
