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Dive into the research topics where David Mease is active.

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Featured researches published by David Mease.


Technometrics | 2006

Extreme (X-)Testing With Binary Data and Applications to Reliability Demonstration

David Mease; Vijayan N. Nair

Reliability demonstration techniques are used to formally verify that the reliability of a product meets a specified target with a certain degree of confidence. When the reliability target to be demonstrated is very high (close to 1), traditional reliability demonstration plans require extremely large sample sizes or have low power. One solution to this problem is to inflate the failure probability by testing products under extreme conditions so that they are more likely to fail. It is sufficient then to demonstrate a lower reliability target that can be mapped back to the required reliability under standard conditions. This article develops a general framework for this type of extreme testing, or “X-testing,” with binary data. The effects of X-testing on sample size and power of reliability demonstration plans are discussed. Properties of various X-transforms are studied with respect to zero-failure plans, fixed sample size plans, and fixed power plans. Conditions under which X-transforms lead to inadmissible or uniformly efficient tests are obtained. X-testing is similar in spirit to accelerated testing as both methods are intended to induce failures, but there are some key differences. Several applications, in addition to reliability demonstration, are used to illustrate the general usefulness of the approach.


Journal of Machine Learning Research | 2007

Boosted Classification Trees and Class Probability/Quantile Estimation

David Mease; Abraham J. Wyner; Andreas Buja


Journal of Machine Learning Research | 2008

Evidence Contrary to the Statistical View of Boosting

David Mease; Abraham J. Wyner


Statistica Sinica | 2006

Unique optimal partitions of distributions and connections to hazard rates and stochastic ordering

David Mease; Vijayan N. Nair


Journal of Machine Learning Research | 2017

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

Abraham J. Wyner; Matthew Olson; Justin Bleich; David Mease


Technometrics | 2004

Selective Assembly in Manufacturing

David Mease; Vijayan N. Nair; Agus Sudjianto


Archive | 2008

Evidence Contrary to the Statistical View of Boosting: A Rejoinder to Responses

David Mease; Abraham J. Wyner


Statistical Science | 2007

Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting

Andreas Buja; David Mease; Abraham J. Wyner


Quality Engineering | 2008

Latin hyperrectangle sampling for computer experiments

David Mease; Derek Bingham


Statistical Science | 2007

Boosting Algorithms: Regularization, Prediction and Model Fitting. Comment.

Andreas Buja; David Mease; Abraham J. Wyner

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Abraham J. Wyner

University of Pennsylvania

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Andreas Buja

University of Pennsylvania

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Matthew Olson

University of Pennsylvania

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