Michael Naaman, Ph.D., is a senior consultant specializing in antitrust, econometrics, and machine learning. He has provided economic and econometric analysis in issues relating to patent infringement and intellectual property, false advertising, and antitrust disputes.
In intellectual property disputes, Dr. Naaman has experience using hedonic regression models to determine the profitability of patented features of products. In antitrust matters, Dr. Naaman has analyzed various industries, including broiler chickens, optical disk drives, liquid crystal displays, resistors, trucking, automobile components, agriculture, online advertising, home appliances, animation, and credit cards. He has extensive experience applying econometric techniques to assess issues such as liability, overcharge, and pass-through.
Dr. Naaman has also published on statistical and econometric topics. In a paper published in the Electronic Journal of Statistics, he developed Almost Sure hypothesis testing and resolved the Jeffreys-Lindley paradox, which puzzled statisticians for over sixty years. He has also made contributions to the field of optimization, and has been invited to present his theories at the World Congress of Global Optimization. He has applied these optimization ideas to prove the multivariate Dvoretzky-Kiefer-Wolfowitz (DKW) inequality; work which led to the world’s first practical nonparametric test for multivariate probability distributions.
Dr. Naaman has a decade of experience in the economic consulting industry. He received his Ph.D. in economics from Rice University, and he received a M.S. in statistics and B.S. in economics, math, and physics from Tulane University.
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