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- David H. Bailey

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
48
Citations
12,832
201
World Ranking
873
National Ranking
427

- Operating system
- Statistics
- Mathematical analysis

David H. Bailey mainly focuses on Algorithm, Parallel computing, Computation, Floating point and Extended precision. His studies deal with areas such as Transcendental function, Number theory, Arithmetic, Experimental mathematics and Subroutine as well as Algorithm. His Parallel computing study incorporates themes from Fast Fourier transform, Latency and Scalability.

His Computation research integrates issues from Software, Computer engineering and Code. The various areas that David H. Bailey examines in his Extended precision study include Arbitrary-precision arithmetic and Computational science. His biological study spans a wide range of topics, including Applied research, Set and Parallel processing.

- The Nas Parallel Benchmarks (2023 citations)
- IEEE Standard for Floating-Point Arithmetic (610 citations)
- The NAS parallel benchmarks—summary and preliminary results (433 citations)

The scientist’s investigation covers issues in Computation, Experimental mathematics, Parallel computing, Sharpe ratio and Econometrics. His Computation study also includes fields such as

- Software which intersects with area such as Language translation,
- Computer engineering which is related to area like Floating point. David H. Bailey combines subjects such as Theoretical computer science, Algebraic number and Quantum field theory with his study of Experimental mathematics.

His work carried out in the field of Parallel computing brings together such families of science as Fast Fourier transform and Set. His Sharpe ratio research is multidisciplinary, relying on both Multiple comparisons problem and Selection bias. His Arbitrary-precision arithmetic research entails a greater understanding of Algorithm.

- Computation (16.90%)
- Experimental mathematics (14.14%)
- Parallel computing (11.38%)

- Sharpe ratio (18.62%)
- Overfitting (13.79%)
- Econometrics (16.90%)

His primary scientific interests are in Sharpe ratio, Overfitting, Econometrics, Investment strategy and Computation. The study incorporates disciplines such as Multiple comparisons problem, Modern portfolio theory and Selection bias in addition to Sharpe ratio. His work in the fields of Econometrics, such as Econometric methods, overlaps with other areas such as Market forecast.

His research integrates issues of Numerical integration, Algebra, Arithmetic and Pure mathematics in his study of Computation. A large part of his Arithmetic studies is devoted to Arbitrary-precision arithmetic. His Experimental mathematics research includes elements of Theoretical computer science and Calculus.

- Enhancing reproducibility for computational methods. (179 citations)
- PSEUDO-MATHEMATICS AND FINANCIAL CHARLATANISM: THE EFFECTS OF BACKTEST OVERFITTING ON OUT-OF-SAMPLE PERFORMANCE (73 citations)
- The NAS Parallel Benchmarks 2.0 (55 citations)

- Operating system
- Statistics
- Mathematical analysis

David H. Bailey mainly focuses on Overfitting, Investment strategy, Sharpe ratio, Econometrics and Context. His work often combines Overfitting and Investment management studies. Many of his Investment strategy research pursuits overlap with Finance, Small number and Out of sample.

David H. Bailey has researched Econometrics in several fields, including Selection bias and Portfolio. His Portfolio research is multidisciplinary, incorporating elements of Market data, Nonparametric statistics and Regression. His Context research spans across into areas like Minifloat, Mathematical physics, Arbitrary-precision arithmetic, Arithmetic and Empirical data.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

The Nas Parallel Benchmarks

D.H. Bailey;E. Barszcz;J.T. Barton;D.S. Browning.

ieee international conference on high performance computing data and analytics **(1991)**

3355 Citations

IEEE Standard for Floating-Point Arithmetic

Dan Zuras;Mike Cowlishaw;Alex Aiken;Matthew Applegate.

IEEE Std 754-2008 **(2008)**

663 Citations

On the rapid computation of various polylogarithmic constants

David Bailey;Peter Borwein;Simon Plouffe.

Mathematics of Computation **(1997)**

471 Citations

FFTs in external or hierarchical memory

D. H. Bailey.

The Journal of Supercomputing **(1990)**

431 Citations

Experimentation in mathematics : computational paths to discovery

Jonathan M. Borwein;David H. Bailey;Roland Girgensohn.

**(2004)**

410 Citations

The fractional Fourier transform and applications

David H. Bailey;Paul N. Swarztrauber.

Siam Review **(1991)**

408 Citations

Algorithms for quad-double precision floating point arithmetic

Y. Hida;X.S. Li;D.H. Bailey.

symposium on computer arithmetic **(2001)**

343 Citations

Precimonious: tuning assistant for floating-point precision

Cindy Rubio-González;Cuong Nguyen;Hong Diep Nguyen;James Demmel.

ieee international conference on high performance computing data and analytics **(2013)**

305 Citations

Design, implementation and testing of extended and mixed precision BLAS

Xiaoye S. Li;James W. Demmel;David H. Bailey;Greg Henry.

ACM Transactions on Mathematical Software **(2002)**

297 Citations

Enhancing reproducibility for computational methods.

Victoria Stodden;Marcia McNutt;David H. Bailey;Ewa Deelman.

Science **(2016)**

294 Citations

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