Search Results - "Vardeman, Stephen B."

Manipulating Stress Responses during Spaceflight Training with Virtual Stressors by Finseth, Tor, Dorneich, Michael C., Keren, Nir, Franke, Warren D., Vardeman, Stephen B.

“…Virtual reality (VR) provides the ability to simulate stressors to replicated real-world situations. It allows for the creation and validation of training,…”
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Some Bayesian biclustering methods: Modeling and inference by Chakraborty, Abhishek, Vardeman, Stephen B.

“…Standard one‐way clustering methods form homogeneous groups in a set of objects. Biclustering (or, two‐way clustering) methods simultaneously cluster rows and…”
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Modeling and inference for mixtures of simple symmetric exponential families of p‐dimensional distributions for vectors with binary coordinates by Chakraborty, Abhishek, Vardeman, Stephen B.

“…We propose tractable symmetric exponential families of distributions for multivariate vectors of 0's and 1's in p dimensions, or what are referred to in this…”
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Modern measurement, probability, and statistics: Some generalities and multivariate illustrations by Vardeman, Stephen B.

“…In broad terms, effective probability modeling of modern measurement requires the development of (usually parametric) distributions for increasingly complex…”
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Bayesian Inference for a New Class of Distributions on Equivalence Classes of Three-Dimensional Orientations With Applications to Materials Science by Du, Chuanlong, Nordman, Daniel J., Vardeman, Stephen B.

“…Experiments in materials science investigating cubic crystalline structures often collect data which are in truth equivalence classes of crystallographically…”
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Modeling and Inference for Measured Crystal Orientations and a Tractable Class of Symmetric Distributions for Rotations in Three Dimensions by Bingham, Melissa A., Nordman, Daniel J., Vardeman, Stephen B.

“…Electron backscatter diffraction (EBSD) is a technique used in materials science to study the microtexture of metals, producing data that measure the…”
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Majority Voting by Independent Classifiers Can Increase Error Rates by Vardeman, Stephen B., Morris, Max D.

“…The technique of "majority voting" of classifiers is used in machine learning with the aim of constructing a new combined classification rule that has better…”
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A Bayesian Approach to the Analysis of Gauge R&R Data by Weaver, Brian P., Hamada, Michael S., Vardeman, Stephen B., Wilson, Alyson G.

“…Gauge repeatability and reproducibility (R&R) studies are used to assess precision of measurement systems. In particular, they are used to quantify the…”
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ASA President’s Task Force Statement on Statistical Significance and Replicability by Yoav Benjamini, Richard De Veaux, Bradley Efron, Scott Evans, Mark Glickman, Barry I. Graubard, Xuming He, Xiao-Li Meng, Nancy Reid, Stephen M. Stigler, Stephen B. Vardeman, Christopher K. Wikle, Tommy Wright, Linda J. Young, Karen Kafadar

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Bayes Statistical Analyses for Particle Sieving Studies by Leyva, Norma, Page, Garritt L., Vardeman, Stephen B., Wendelberger, Joanne R.

“…Particle size is commonly used to determine quality and predict performance of particle systems. We consider particle size distributions inferred from a…”
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A Bayesian Approach to Establishing a Reference Particle Size Distribution in the Presence of Outliers by Page, Garritt L., Vardeman, Stephen B.

“…The presence of observations or measurements that are unlike the majority is fairly common in studies conducted to establish particle size (or weight fraction)…”
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Bayes Inference for a Tractable New Class of Non-symmetric Distributions for 3-Dimensional Rotations by Bingham, Melissa A., Nordman, Daniel J., Vardeman, Stephen B.

“…Both existing models for non-symmetric distributions on 3-dimensional rotations and their associated one-sample inference methods have serious limitations in…”
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Finite-sample investigation of likelihood and Bayes inference for the symmetric von Mises–Fisher distribution by Bingham, Melissa A., Nordman, Daniel J., Vardeman, Stephen B.

“…We consider likelihood and Bayes analyses for the symmetric matrix von Mises–Fisher (matrix Fisher) distribution, which is a common model for three-dimensional…”
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Using Bayes methods and mixture models in inter-laboratory studies with outliers by Page, Garritt L., Vardeman, Stephen B.

“…Inter-laboratory studies (especially so-called key comparisons) are conducted to evaluate both national and international equivalence of measurement. In these…”
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Uniformly Hyper-Efficient Bayes Inference in a Class of Nonregular Problems by Nordman, Daniel J., Vardeman, Stephen B., Bingham, Melissa A.

“…We present a tractable class of nonregular continuous statistical models where 1) likelihoods have multiple singularities and ordinary maximum likelihood is…”
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Bayes one-sample and one-way random effects analyses for 3-D orientations with application to materials science by Bingham, Melissa A., Nordman, Daniel J., Vardeman, Stephen B.

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Elementary Statistical Methods and Measurement Error by Vardeman, Stephen B., Wendelberger, Joanne R., Burr, Tom, Hamada, Michael S., Moore, Leslie M., Jobe, J. Marcus, Morris, Max D., Wu, Huaiqing

“…How the sources of physical variation interact with a data collection plan determines what can be learned from the resulting dataset, and in particular, how…”
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Statistical Intervals: A Guide for Practitioners and Researchers, Second Edition, William Q.Meeker, Gerald J.Hahn and Luis A.Escobar, John Wiley & Sons Inc., 2017, 592 + xxxv pages, £83.50, hardcover, ISBN: 978‐0‐471‐68717‐7 by Vardeman, Stephen B.

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Modeling and inference for mixtures of simple symmetric exponential families of ‐dimensional distributions for vectors with binary coordinates by Chakraborty, Abhishek, Vardeman, Stephen B.

“…We propose tractable symmetric exponential families of distributions for multivariate vectors of 0's and 1's in dimensions, or what are referred to in this…”
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Statistics and Ethics: Some Advice for Young Statisticians by Vardeman, Stephen B, Morris, Max D

“…We write to young statisticians about the nature of statistics and their responsibilities as members ofthe statistical profession. We observe that the practice…”
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