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J.D. Opdyke's Page

Profile Information

Short Bio:
J.D. Opdyke is President of DataMineIt, a statistical consultancy. J.D. has over 20 years of experience providing profitable statistical data mining, econometric analysis, and algorithm development to the banking, credit, and consulting sectors. Clients include multiple Fortune 50 financial firms, including some of the largest banks and credit card firms globally, for whom J.D. has tackled numerous “big data” analytical challenges and increased speeds of computationally intensive econometric and statistical models by orders of magnitude using SAS®. J.D.’s empirical risk analytics work includes operational risk, credit risk, market risk, and model risk. He has presented expert testimony on applied econometrics in large litigations ($0.4 billion), and has published nine peer-reviewed journal papers (eight as sole author) spanning applied statistics, statistical finance, number theory/combinatorics, computational statistics, and applied econometrics. J.D. earned his undergraduate degree, with honors, from Yale University, his Master’s degree from Harvard University where he was both a Kennedy Fellow and a Social Policy Research Fellow, and he has completed post-graduate statistics work as an Advanced Study Program Fellow in the graduate mathematics department at MIT.
My Website or LinkedIn Profile (URL):
http://www.DataMineIt.com
Field of Expertise:
Business Analytics, Predictive Modeling, Data Mining, Econometrics, Statistical Consulting, Finance, SAS
Years of Experience in Analytical Role:
20+
Professional Status:
Director, C-Level, Consultant
Interests:
Finding a New Position, Networking, New Venture
What is your Favorite Data Mining or Analytical Website?
http://www.DataMineIt.com
What Other Analytical Website do you Recommend?
http://www.Google.com
Your Company:
DataMineIt
Industry:
Statistical Data Mining - Applied Statistics, Econometrics, Algorithmics
How did you find out about AnalyticBridge?
DataShaping

J.D. Opdyke's Blog

Bootstraps, Permutation Tests, and Sampling Orders of Magnitude Faster Using SAS®, John Douglas (“J.D.”) Opdyke

Posted on September 16, 2013 at 8:27am 0 Comments

Bootstraps, Permutation Tests, and Sampling Orders of Magnitude Faster Using SAS, Computational Statistics-WIREs, Vol. 5, Issue 5, 391-405.  Download @ http://www.datamineit.com/DMI_publications.htm



While permutation tests and bootstraps have very wide-ranging application, both share a common potential drawback: as data-intensive resampling methods, both can be runtime prohibitive when applied to large or even…

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J.D. Opdyke, Author: A Powerful and Robust Nonparametric Statistic for Joint Mean-Variance Quality Control

Posted on March 9, 2012 at 7:41am 0 Comments

For statistical process control, a number of single charts that jointly monitor both process mean and variability recently have been developed. For quality control-related hypothesis testing, however, there has been little analogous development of joint mean-variance tests: only one two-sample statistic that is not computationally intensive has been designed specifically for the one-sided test of Ho: Mean2<=Mean1 and StDev2<=StDev1 vs. Ha: Mean2>Mean1 OR StDev2>StDev1 (see…

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J.D. Opdyke, Author: A Unified Approach to Algorithms Generating Unrestricted and Restricted Integer Compositions and Integer Partitions, J. of Mathematical Modelling and Algorithms, 2010, 9(1), 53-97

Posted on February 15, 2012 at 10:47am 0 Comments

An original algorithm is presented that generates both restricted integer compositions and restricted integer partitions that can be constrained simultaneously by a) upper and lower bounds on the number of summands (“parts”) allowed, and b) upper and lower bounds on the values of those parts.  The algorithm is recursive, based directly on very fundamental mathematical constructs, and reasonably fast with good time complexity.  General solutions to the open problems of counting the number of…

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J.D. Opdyke, Author: A Unified Approach to Algorithms Generating Unrestricted and Restricted Integer Compositions and Integer Partitions, J. of Mathematical Modelling and Algorithms, 2010, 9(1), 53-97

Posted on February 15, 2012 at 10:34am 0 Comments

An original algorithm is presented that generates both restricted integer compositions and restricted integer partitions that can be constrained simultaneously by a) upper and lower bounds on the number of summands (“parts”) allowed, and b) upper and lower bounds on the values of those parts.  The algorithm is recursive, based directly on very fundamental mathematical constructs, and reasonably fast with good time complexity.  General solutions to the open problems of counting the number of…

Continue

J.D. Opdyke, Author: Bootstraps, Permutation Tests, and Sampling With and Without Replacement Orders of Magnitude Faster Using SAS®

Posted on February 12, 2012 at 9:30am 0 Comments

A very efficient approach to random sampling in SAS® achieves speed increases orders of magnitude faster than the relevant "built-in" SAS® procedures. For sampling with replacement as applied to bootstraps, seven algorithms are compared, and the fastest ("OPDY"), based on the new approach, achieves speed increases over 220x faster than Proc SurveySelect. OPDY also handles datasets many times larger than those on which two hashing algorithms crash. For sampling without replacement as applied…

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