J.D. Opdyke
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  • Marblehead, MA
  • United States
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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 100 financial firms, including some of the largest banks and credit card firms globally. 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.
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J.D. Opdyke's Blog

Bootstraps, Permutation Tests, and Sampling Orders of Magnitude Faster Using SAS

Posted on September 16, 2013 at 5:25am 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…


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

Posted on March 9, 2012 at 5:42am 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…


Estimating Operational Risk Capital: the Challenges of Truncation, the Hazards of MLE, and the Promise of Robust Statistics

Posted on February 10, 2012 at 7:30pm 0 Comments

J.D. Opdyke and Alex Cavallo

In operational risk measurement, the estimation of severity distribution parameters is the main driver of capital estimates, yet this remains a non-trivial challenge for many reasons.  Maximum likelihood estimation (MLE) does not adequately meet this challenge because of its well-documented non-robustness to modest violations of idealized textbook model assumptions, specifically that the data are independent and identically distributed (i.i.d.), which is…


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