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The estimation of parameters in nonlinear, implicit measurement error models with experiment-wide measurements

Anderson, K.K. - 2008

Measurement error modeling is a statistical approach to the estimation of unknown model parameters which takes into account the measurement errors in all of the data. Approaches which ignore the measurement errors in so-called independent variables may yield inferior estimates of unknown model...

Persistent link: https://www.econbiz.de/10009435461

Effect of initial seed and number of samples on simple-random and Latin-Hypercube Monte Carlo probabilities (confidence interval considerations)

ROMERO,VICENTE J. - 2008

In order to devise an algorithm for autonomously terminating Monte Carlo sampling when sufficiently small and reliable confidence intervals (CI) are achieved on calculated probabilities, the behavior of CI estimators must be characterized. This knowledge is also required in comparing the...

Persistent link: https://www.econbiz.de/10009435548

Experimental uncertainty estimation and statistics for data having interval uncertainty.

Kreinovich, Vladik; Oberkampf, William Louis; Ginzburg, Lev - 2008

intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than …, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics … widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as …

Persistent link: https://www.econbiz.de/10009435587

A further study of the impact of bias on accountability

Harvey, T.R.; McGuire, S.A. - 2009

When measurements are obtained for the purpose of material accountability, any deviation of averages from true value is one of great concern. The key term here is true value. Any deviation of averages from their true value will result in inventory discrepancies. It will not matter that the bias...

Persistent link: https://www.econbiz.de/10009435607

Two-stage sampling for acceptance testing

Atwood, C.L.; Bryan, M.F. - 2008

Sometimes a regulatory requirement or a quality-assurance procedure sets an allowed maximum on a confidence limit for a mean. If the sample mean of the measurements is below the allowed maximum, but the confidence limit is above it, a very widespread practice is to increase the sample size and...

Persistent link: https://www.econbiz.de/10009435858

Two-stage sampling for acceptance testing

Atwood, C.L.; Bryan, M.F. - 2008

Persistent link: https://www.econbiz.de/10009436257

Measurement uncertainty analysis techniques applied to PV performance measurements

Wells, C. - 2009

The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty...

Persistent link: https://www.econbiz.de/10009436378

Handling uncertainty in quantitative estimates in integrated resource planning

Tonn, B.E.; Wagner, C.G. - 2009

This report addresses uncertainty in Integrated Resource Planning (IRP). IRP is a planning and decisionmaking process employed by utilities, usually at the behest of Public Utility Commissions (PUCs), to develop plans to ensure that utilities have resources necessary to meet consumer demand at...

Persistent link: https://www.econbiz.de/10009436560

Uncertainty vs. Interindividual variability

Bogen, K.T. - 2008

Distinct treatment of uncertainty and interindividual variability in variates used to model risk ensures that quantitative assessments of these attributes in modeled risk are maximally relevant to potential regulatory concerns. For example, such a distinction is required for quantitative...

Persistent link: https://www.econbiz.de/10009436588

A procedure for assessing uncertainty in models

McKay, M.D.; Beckman, R.J. - 2008

This paper discusses uncertainty in the output calculation of a model due to uncertainty in inputs values. Uncertainty in input values, characterized by suitable probability distributions, propagates through the model to a probability distribution of an output. Our objective in studying...

Persistent link: https://www.econbiz.de/10009436920