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Displaying records 41 to 50 of 116 records.
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41. Consistency Tests for Key Comparison Data
Topic: Statistics
Published: 5/1/2004
Authors: Dominic F. (Dominic F.) Vecchia, Chih-Ming Wang
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=150569

42. Copulas for Uncertainty Analysis
Topic: Statistics
Published: 4/20/2010
Author: Antonio M Possolo
Abstract: Applying the Monte Carlo method for propagation of measurement uncertainty described in the GUM Supplement 1, when the input quantities are correlated, involves the specification of a joint probability distribution for these quantities. In practice, ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=904787

43. DATA ANALYTICS USING SIMULATION FOR SMART MANUFACTURING
Topic: Statistics
Published: 12/11/2014
Authors: Guodong Shao, Sanjay Jain, Seungjun Shin
Abstract: Manufacturing organizations are able to accumulate large amounts of plant floor production and environmental data due to advances in data collection, communications technology, and use of standards. The challenge has shifted from collecting a suffic ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=915917

44. Decision Analysis Methods for Selecting Consumer Services with Attribute Value Uncertainty
Topic: Statistics
Published: 12/1/2013
Authors: Dennis D Leber, Jeffery W Herrmann
Abstract: The basic risky decision is defined as a decision for which an uncertain event, in addition to the alternative selected, defines the outcome. Beyond the uncertain event, additional uncertainties can enter a decision including the uncertainty in the ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=911546

45. Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates
Topic: Statistics
Published: 10/12/2012
Author: Steven P Lund
Abstract: Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-seq data, which frequently in ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=911007

46. Erratum: Statistical Interpretation of Key Comparison Reference Value and Degrees of Equivalence
Series: Journal of Research (NIST JRES)
Topic: Statistics
Published: 8/1/2004
Authors: Raghu N Kacker, Raju Vsnu Datla, Albert C Parr
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=150898

47. Estimating Common Parameters in Heterogeneous Random Effects Models
Topic: Statistics
Published: 4/13/2011
Author: Andrew L Rukhin
Abstract: A question of fundamental importance for meta-analysis of heterogeneous data studies is how to form a best consensus estimator of common parameters, and what uncertainty to attach to the estimate. This issue is addressed for a class of unbalanced lin ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=904659

48. Estimating common mean and heterogeneity variance in two study case meta-analysis
Topic: Statistics
Published: 4/1/2012
Author: Andrew L Rukhin
Abstract: The relative behavior of estimators of the common mean and of the heterogeneity variance in the simplest random effects model of meta-analysis is explored. A simple estimator improving on the DerSimonian-Laird procedure is derived.
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=910111

49. Estimating heterogeneity variance in meta-analysis
Topic: Statistics
Published: 8/6/2012
Author: Andrew L Rukhin
Abstract: Several new estimators of the between-study variability in a heterogeneous random effects meta-analysis model are derived. One of them can be interpreted as the empirical Bayes procedure for a diffuse prior with the given prior mean. Another is th ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=909704

50. Evaluation of Segmentation Algorithms on Cell Populations Using CDF Curves
Topic: Statistics
Published: 2/24/2012
Author: Robert Charles Hagwood
Abstract: Cell segmentation is a critical step in the analysis pipeline for most imaging cytometry experiments and the segmentation algorithm can effect the quantitative data derived from image analysis. Methods to evaluate segmentation algorithms are importan ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=907743



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