Propagation of conformity statements in compliance with the GUM and ISO 17025

This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-04-19 Creation time: 07-32-12 --- Number of references 68 article KlauenbergGF2023 Propagation of conformity statements in compliance with the GUM and ISO 17025 Metrologia 2023 9 22 60 5 054004 8.4,8.42,Messunsicherheit 10.1088/1681-7575/acf3eb KKlauenberg JGreenwood GFoyer article BrahmaKMSK2023 Data-efficient Bayesian learning for radial dynamic MR reconstruction Medical Physics 2023 6 27 8.4,8.42,ML,Messunsicherheit,LargeScaleDataAna 10.1002/mp.16543 SBrahma CKolbitsch JMartin TSchäffter AKofler article MarschallWBE2023 On modelling of artefact instability in interlaboratory comparisons Metrologia 2023 6 26 8.4,8.42,KC,Messunsicherheit accepted 10.1088/1681-7575/ace18f MMarschall GWübbeler MBorys CElster article KokDWE2023 Virtual experiments for the assessment of data analysis and uncertainty quantification methods in scatterometry Metrologia 2023 5 19 8.4,8.42,Messunsicherheit,Form accepted 10.1088/1681-7575/acd6fd GKok v MDijk GWübbeler CElster article KokWE2022 Impact of Imperfect Artefacts and the Modus Operandi on Uncertainty Quantification Using Virtual Instruments Metrology 2022 6 12 2 311--319 8.4,8.42,Messunsicherheit,Form 10.3390/metrology2020019 GKok GWübbeler CElster article MarschallSSE2022 Uncertainty propagation in quantitative magnetic force microscopy using a Monte-Carlo method IEEE Transactions on Magnetics 2022 2 21 1--1 8.4,8.42,Messunsicherheit 10.1109/TMAG.2022.3153176 MMarschall SSievers H WSchumacher CElster article MarschallWE2022 Rejection sampling for Bayesian uncertainty evaluation using the Monte Carlo techniques of GUM-S1 Metrologia 2022 2 1 59 1 015004 8.4,8.42,Messunsicherheit 10.1088/1681-7575/ac3920 MMarschall GWübbeler CElster article GruberDSEE2022 Discrete wavelet transform on uncertain data: Efficient online implementation for practical applications. Advanced Mathematical and Computational Tools in Metrology and Testing XII, Series on Advances in Mathematics for Applied Sciences 2022 1 30 90 8.4,8.42,Messunsicherheit 978-981-1242-37-3 MGruber TDorst ASchütze SEichstädt CElster phdthesis Metzner2021 Bayesian data analysis for magnetic resonance fingerprinting 2021 12 7 publiziert 8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna TU Berlin PhD Thesis 10.14279/depositonce-12455 SMetzner article MartinE2021 GUI for Bayesian sample size planning in type A uncertainty evaluation Measurement Science and Technology 2021 4 30 32 7 5005 8.4,8.42,Messunsicherheit 10.1088/1361-6501/abe2bd JMartin CElster article MetznerWFGKE2021 Bayesian uncertainty quantification for magnetic resonance fingerprinting Physics in Medicine & Biology 2021 3 1 66 7 075006 8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna 10.1088/1361-6560/abeae7 SMetzner GWübbeler SFlassbeck CGatefait CKolbitsch CElster article WubbelerME2020 A simple method for Bayesian uncertainty evaluation in linear models Metrologia 2020 10 21 57 6 065010 8.4,8.42,Unsicherheit 10.1088/1681-7575/aba3b8 GWübbeler MMarschall CElster article MartinE2020_2 The variation of the posterior variance and Bayesian sample size determination Statistical Methods & Applications 2020 8 25 1613-981X 8.4,8.42,Unsicherheit 10.1007/s10260-020-00545-3 JMartin CElster article DemeyerFE2020 Guidance on Bayesian uncertainty evaluation for a class of GUM measurement models Metrologia 2020 8 18 8.4,8.42,Unsicherheit 10.1088/1681-7575/abb065 SDemeyer NFischer CElster article BartlEMSVW2020 Thermal expansion and compressibility of single-crystal silicon between 285 K and 320 K Measurement Science and Technology 2020 4 3 31 6 8.4,8.42,Unsicherheit,Regression 10.1088/1361-6501/ab7359 GBartl CElster JMartin RSchödel MVoigt AWalkov article WubbelerE2020 On the transferability of the GUM-S1 type A uncertainty Metrologia 2020 1 23 57 1 8.4,8.42,Unsicherheit 10.1088/1681-7575/ab50d6 GWübbeler CElster article KlauenbergWE2019 About not Correcting for Systematic Effects Measurement Science Review 2019 9 30 19 5 204--208 8.4,8.42,Unsicherheit 10.2478/msr-2019-0026 KKlauenberg GWübbeler CElster article MetznerWE2018 Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging AStA Adv Stat Anal 2019 8 29 103 3 333--355 8.4,8.42,Messunsicherheit,Regression,Fingerprinting,LargeScaleDataAna 10.1007/s10182-018-00334-0 SMetzner GWübbeler CElster article MartinBE2019 Application of Bayesian model averaging to the determination of thermal expansion of single-crystal silicon Measurement Science and Technology 2019 2 21 30 045012 8.4,8.42,Unsicherheit,Regression 10.1088/1361-6501/ab094b JMartin GBartl CElster article BodnarE2016 Assessment of vague and noninformative priors for Bayesian estimation of the realized random effects in random-effects meta-analysis AStA Advances in Statistical Analysis 2018 1 31 102 1 1--20 8.42,KC,Unsicherheit 10.1007/s10182-016-0279-7 OBodnar CElster article WubbelerBE2018 Robust Bayesian linear regression with application to an analysis of the CODATA values for the Planck constant Metrologia 2018 1 2 55 1 20 8.4,8.42,Unsicherheit,Regression 10.1088/1681-7575/aa98aa GWübbeler OBodnar CElster article SchmahlingWKRSTSE2017 Uncertainty evaluation and propagation for spectral measurements Color, Research and Application 2017 9 26 43 1 6--16 8.4,8.42,Unsicherheit,LargeScaleDataAna 10.1002/col.22185 FSchmähling GWübbeler UKrüger BRuggaber FSchmidt R DTaubert ASperling CElster article ReginattoAE2017 Assessment of CT image quality using a Bayesian approach Metrologia 2017 6 14 54 4 S74--S82 8.4,8.42,Unsicherheit 10.1088/1681-7575/aa735b MReginatto MAnton CElster article EichstadtW2017 Evaluation of uncertainty for regularized deconvolution: A case study in hydrophone measurements J. Acoust. Soc. Am. 2017 6 6 141 6 4155--4167 8.4,8.42,Unsicherheit,Dynamik 10.1121/1.4983827 SEichstädt VWilkens article BodnarBE2017 Bayesian inference for measurements of ionizing radiation under partial information Metrologia 2017 5 11 54 3 S29--S33 8.4,8.42,Unsicherheit 10.1088/1681-7575/aa69ad OBodnar RBehrens CElster article EichstadtESE2017 Evaluation of dynamic measurement uncertainty – an open-source software package to bridge theory and practice J. Sens. Sens. Syst. 2017 2 14 6 97-105 8.4,8.42,Unsicherheit,Dynamik 10.5194/jsss-6-97-2017 SEichstädt CElster I MSmith T JEsward article BodnarLAPE2017 Bayesian estimation in random effects meta-analysis using a non-informative prior Statistics in Medicine 2017 2 1 39 2 378--399 8.4,8.42,KC,Unsicherheit 1097-0258 10.1002/sim.7156 OBodnar ALink BArendacká APossolo CElster article KlauenbergE2017 Sampling for assurance of future reliability Metrologia 2017 1 2 54 1 59--68 8.42, Unsicherheit 8.42, Unsicherheit, Stichprobenverf 10.1088/1681-7575/54/1/59 KKlauenberg CElster article WubbelerCE2016 Evaluation of uncertainties for CIELAB color coordinates Color Research & Application 2016 12 31 8.4,8.42,Unsicherheit 10.1002/col.22109 GWübbeler JCampos Acosta CElster article Bodnar2016b Evaluation of uncertainty in the adjustment of fundamental constants Metrologia 2016 1 5 53 1 S46 Combining multiple measurement results for the same quantity is an important task in metrology and in many other areas. Examples include the determination of fundamental constants, the calculation of reference values in interlaboratory comparisons, or the meta-analysis of clinical studies. However, neither the GUM nor its supplements give any guidance for this task. Various approaches are applied such as weighted least-squares in conjunction with the Birge ratio or random effects models. While the former approach, which is based on a location-scale model, is particularly popular in metrology, the latter represents a standard tool used in statistics for meta-analysis. We investigate the reliability and robustness of the location-scale model and the random effects model with particular focus on resulting coverage or credible intervals. The interval estimates are obtained by adopting a Bayesian point of view in conjunction with a non-informative prior that is determined by a currently favored principle for selecting non-informative priors. Both approaches are compared by applying them to simulated data as well as to data for the Planck constant and the Newtonian constant of gravitation. Our results suggest that the proposed Bayesian inference based on the random effects model is more reliable and less sensitive to model misspecifications than the approach based on the location-scale model. 8.42, Unsicherheit http://stacks.iop.org/0026-1394/53/i=1/a=S46 10.1088/0026-1394/53/1/S46 OBodnar CElster JFischer APossolo BToman article Klauenberg2016 Markov chain Monte Carlo methods: an introductory example Metrologia 2016 1 3 53 1 S32 When the Guide to the Expression of Uncertainty in Measurement (GUM) and methods from its supplements are not applicable, the Bayesian approach may be a valid and welcome alternative. Evaluating the posterior distribution, estimates or uncertainties involved in Bayesian inferences often requires numerical methods to avoid high-dimensional integrations. Markov chain Monte Carlo (MCMC) sampling is such a method---powerful, flexible and widely applied. Here, a concise introduction is given, illustrated by a simple, typical example from metrology. The Metropolis--Hastings algorithm is the most basic and yet flexible MCMC method. Its underlying concepts are explained and the algorithm is given step by step. The few lines of software code required for its implementation invite interested readers to get started. Diagnostics to evaluate the performance and common algorithmic choices are illustrated to calibrate the Metropolis--Hastings algorithm for efficiency. Routine application of MCMC algorithms may be hindered currently by the difficulty to assess the convergence of MCMC output and thus to assure the validity of results. An example points to the importance of convergence and initiates discussion about advantages as well as areas of research. Available software tools are mentioned throughout. Bayesian, MCMC, Markov chain Monte Carlo 8.42, Unsicherheit, Regression http://stacks.iop.org/0026-1394/53/i=1/a=S32 10.1088/0026-1394/53/1/S32 KKlauenberg CElster article Elster2016a Bayesian regression versus application of least squares—an example Metrologia 2016 1 2 53 1 S10 Regression is an important task in metrology and least-squares methods are often applied in this context. Bayesian inference provides an alternative that can take into account available prior knowledge. We illustrate similarities and differences of the two approaches in terms of a particular nonlinear regression problem. The impact of prior knowledge utilized in the Bayesian regression depends on the amount of information contained in the data, and by considering data sets with different signal-to-noise ratios the relevance of the employed prior knowledge for the results is investigated. In addition, properties of the two approaches are explored in the context of the particular example. 8.4, 8.42, Unsicherheit, Regression http://stacks.iop.org/0026-1394/53/i=1/a=S10 10.1088/0026-1394/53/1/S10 CElster GWübbeler article Bodnar2015 Objective Bayesian Inference for a Generalized Marginal Random Effects Model Bayesian Analysis 2016 1 1 11 1 25-45 Open Access objective Bayesian inference,random effects model,reference prior 8.42, Unsicherheit http://projecteuclid.org/euclid.ba/1423083638 International Society for Bayesian Analysis 1931-6690 10.1214/14-BA933 OBodnar ALink CElster article Klauenberg2015_3 A tutorial on Bayesian Normal linear regression Metrologia 2015 1 7 52 6 878--892 Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view.Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach.These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations. 8.42, Regression, Unsicherheit 10.1088/0026-1394/52/6/878 KKlauenberg GWübbeler BMickan PHarris CElster techreport NEW04_Bayes A Guide to Bayesian Inference for Regression Problems 2015 1 6 Regression, 8.42, Unsicherheit fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/BPGWP1.pdf http://www.ptb.de/emrp/new04.html EMRP NEW04 CElster KKlauenberg MWalzel P MHarris M GCox CMatthews LWright AAllard NFischer SEllison PWilson FPennecchi G J PKok AVan der Veen LPendrill article Wubbeler2015 Explanatory power of degrees of equivalence in the presence of a random instability of the common measurand Metrologia 2015 1 3 52 2 400--405 8.42, Unsicherheit, KC http://iopscience.iop.org/article/10.1088/0026-1394/52/2/400 IOP Publishing en 0026-1394 10.1088/0026-1394/52/2/400 GWübbeler OBodnar BMickan CElster article Kok2015 Bayesian analysis of a flow meter calibration problem Metrologia 2015 1 2 52 2 392-399 Regression, 8.42, Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/52/2/392 IOP Publishing 0026-1394 10.1088/0026-1394/52/2/392 G J PKok A M Hvan der Veen P MHarris I MSmith CElster article Possolo2014 Evaluating the uncertainty of input quantities in measurement models Metrologia 2014 51 3 339--353 8.42,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/51/3/339 IOP Publishing en 0026-1394 10.1088/0026-1394/51/3/339 APossolo CElster article Bodnar2014a Analytical derivation of the reference prior by sequential maximization of Shannon's mutual information in the multi-group parameter case Journal of Statistical Planning and Inference 2014 147 106--116 We provide an analytical derivation of a non-informative prior by sequential maximization of Shannon's mutual information in the multi-group parameter case assuming reasonable regularity conditions. We show that the derived prior coincides with the reference prior proposed by Berger and Bernardo, and that it can be considered as a useful alternative expression for the calculation of the reference prior. In using this expression we discuss the conditions under which an improper reference prior can be uniquely defined, i.e. when it does not depend on the particular choice of nested sequences of compact subsets of the parameter space needed for its construction. We also present the conditions under which the reference prior coincides with Jeffreys' prior. Bayes,Reference prior,Shannon's mutual information,statistics 8.42, Unsicherheit http://www.sciencedirect.com/science/article/pii/S0378375813002802 03783758 10.1016/j.jspi.2013.11.003 OBodnar CElster article Bodnar2014 On the adjustment of inconsistent data using the Birge ratio Metrologia 2014 51 5 516--521 8.42,KC,Regression, Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/51/5/516 IOP Publishing en doi:10.1088/0026-1394/51/5/516 0026-1394 10.1088/0026-1394/51/5/516 OBodnar CElster article Arendacka2014a Linear Mixed Models: Gum and Beyond Measurement Science Review 2014 14 2 52-61 In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneﬁcial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers. dynamic measurement, acceleration, dynamic calibration, mixed model, design of experiment 8.42, Dynamik, Unsicherheit fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/epjconf_icm2014_00003.pdf http://www.degruyter.com/view/j/msr.2014.14.issue-2/msr-2014-0009/msr-2014-0009.xml 1335-8871 10.2478/msr-2014-0009 BArendacká ATäubner SEichstädt TBruns CElster article Haslett2014 The link between the mixed and fixed linear models revisited Statistical Papers 2014 56 3 849--861 mixed linear models,statistics 8.42, Unsicherheit http://link.springer.com/10.1007/s00362-014-0611-9 0932-5026 10.1007/s00362-014-0611-9 S JHaslett SPuntanen BArendacká article Elster2014 Bayesian uncertainty analysis compared with the application of the GUM and its supplements Metrologia 2014 51 4 S159--S166 8.42, Bayesian, Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/51/4/S159 IOP Publishing en 0026-1394 10.1088/0026-1394/51/4/S159 CElster article Wubbeler2013 Simplified evaluation of magnetic field fluctuation thermometry Measurement Science and Technology 2013 24 11 115004 8.42,Bayes,MFFT,Regression, Unsicherheit http://iopscience.iop.org/article/10.1088/0957-0233/24/11/115004 IOP Publishing en 0957-0233 10.1088/0957-0233/24/11/115004 GWübbeler CElster article Eichstaedt2012a Efficient implementation of a Monte Carlo method for uncertainty evaluation in dynamic measurements Metrologia 2012 49 3 401 Measurement of quantities having time-dependent values such as force, acceleration or pressure is a topic of growing importance in metrology. The application of the Guide to the Expression of Uncertainty in Measurement (GUM) and its Supplements to the evaluation of uncertainty for such quantities is challenging. We address the efficient implementation of the Monte Carlo method described in GUM Supplements 1 and 2 for this task. The starting point is a time-domain observation equation. The steps of deriving a corresponding measurement model, the assignment of probability distributions to the input quantities in the model, and the propagation of the distributions through the model are all considered. A direct implementation of a Monte Carlo method can be intractable on many computers since the storage requirement of the method can be large compared with the available computer memory. Two memory-efficient alternatives to the direct implementation are proposed. One approach is based on applying updating formulae for calculating means, variances and point-wise histograms. The second approach is based on evaluating the measurement model sequentially in time. A simulated example is used to compare the performance of the direct and alternative procedures. 8.42, Dynamik, Unsicherheit 10.1088/0026-1394/49/3/401 SEichstädt ALink P MHarris CElster article Klauenberg2012 The multivariate normal mean - sensitivity of its objective Bayesian estimates Metrologia 2012 49 3 395--400 8.42,Bayes,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/49/3/395 IOP Publishing en 0026-1394 10.1088/0026-1394/49/3/395 KKlauenberg CElster article Wubbeler2012 Analysis of magnetic field fluctuation thermometry using Bayesian inference Measurement Science and Technology 2012 23 12 125004 8.42,Bayes,MFFT,Regression, Unsicherheit http://iopscience.iop.org/article/10.1088/0957-0233/23/12/125004 IOP Publishing en 0957-0233 10.1088/0957-0233/23/12/125004 GWübbeler FSchmähling JBeyer JEngert CElster article Bich2012 Revision of the "Guide to the Expression of Uncertainty in Measurement" Metrologia 2012 49 6 702--705 8.42,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/49/6/702 IOP Publishing en 0026-1394 10.1088/0026-1394/49/6/702 WBich M GCox RDybkaer CElster W TEstler BHibbert HImai WKool CMichotte LNielsen LPendrill SSidney A M Hvan der Veen WWöger article Elster2012c On the choice of a noninformative prior for Bayesian inference of discretized normal observations Computational Statistics 2012 27 2 219--235 8.42,Bayes,Unsicherheit http://link.springer.com/10.1007/s00180-011-0251-7 0943-4062 10.1007/s00180-011-0251-7 CElster ILira inbook Wuebbeler2012c Assessment of the GUM S1 Adaptive Monte Carlo Scheme 2012 Advanced Mathematical & Computational Tools in Metrology IX 434 8.42, Unsicherheit F. Pavese, M. Bär, J.M. Limares, C. Perruchet, N.F. Zhang World Scientific New Jersey Series on Advances in Mathematics for Applied Sciences 84 54 GWübbeler P MHarris M GCox CElster inbook Eichstaedt2012e Uncertainty evaluation for continuous-time measurements 2012 Advanced Mathematical & Computational Tools in Metrology and Testing IX  126-135 dynamic measurement, continuous function, stochastic process, uncertainty 8.42, Dynamik, Unsicherheit F. Pavese, M. Bär, J.-R. Filtz, A. B. Forbes, L. Pendrill, K. Shirono World Scientific New Jersey Series on Advances in Mathematics for Applied Sciences 84 16 SEichstädt CElster inbook Esward2012 Uncertainty evaluation for traceable dynamic measurement of mechanical quantities: A case study in dynamic pressure calibration 2012 Advanced Mathematical & Computational Tools in Metrology and Testing IX   143-151 dynamic pressure, calibration, dynamic measurement 8.42, Dynamik, Unsicherheit F. Pavese, M. Bär, J.-R. Filtz, A. B. Forbes, L. Pendrill, K. Shirono World Scientific New Jersey Series on Advances in Mathematics for Applied Sciences 84 19 T JEsward CMatthews SDownes AKnott SEichstädt CElster article Bodnar2011 On the application of Supplement 1 to the GUM to non-linear problems Metrologia 2011 48 5 333--342 8.42,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/48/5/014 IOP Publishing en 0026-1394 10.1088/0026-1394/48/5/014 OBodnar GWübbeler CElster article Elster2011a Bayesian uncertainty analysis for a regression model versus application of GUM Supplement 1 to the least-squares estimate Metrologia 2011 48 5 233--240 8.42, Regression, Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/48/5/001 IOP Publishing en 0026-1394 10.1088/0026-1394/48/5/001 CElster BToman article Wubbeler2010 A two-stage procedure for determining the number of trials in the application of a Monte Carlo method for uncertainty evaluation Metrologia 2010 47 3 317--324 8.42,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/47/3/023 IOP Publishing en 0026-1394 10.1088/0026-1394/47/3/023 GWübbeler P MHarris M GCox CElster article Eichstadt2010k On-line dynamic error compensation of accelerometers by uncertainty-optimal filtering Measurement 2010 43 5 708-713 The output signal of an accelerometer typically contains dynamic errors when a broadband acceleration is applied. In order to determine the applied acceleration, post-processing of the accelerometerâ€™s output signal is required. To this end, we propose the application of a digital FIR filter. We evaluate the uncertainty associated with the filtered output signal and give explicit formulae which allow for on-line calculation. In this way, estimation of the applied acceleration and the calculation of associated uncertainties may be carried out during the measurement. The resulting uncertainties can strongly depend on the design of the applied filter and we describe a simple method to construct an uncertainty-optimal filter. The benefit of the proposed procedures is illustrated by means of simulated measurements. Accelerometer,Digital filter,Dynamic measurements,Dynamik,Uncertainty 8.42, Dynamik, Unsicherheit http://www.sciencedirect.com/science/article/pii/S0263224110000023 10.1016/j.measurement.2009.12.028 SEichstädt ALink TBruns CElster article Link2009b Uncertainty evaluation for IIR (infinite impulse response) filtering using a state-space approach Measurement Science and Technology 2009 20 5 055104 dynamic measurement, digital filter, deconvolution, dynamic uncertainty 8.42,Dynamik, Unsicherheit IOP Publishing 10.1088/0957-0233/20/5/055104 ALink CElster article Elster2009 Bayesian uncertainty analysis under prior ignorance of the measurand versus analysis using the Supplement 1 to the Guide : a comparison Metrologia 2009 46 3 261--266 8.42,Bayes,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/46/3/013 IOP Publishing en 0026-1394 10.1088/0026-1394/46/3/013 CElster BToman inbook Lira2009 Derivation of an output PDF from Bayes theorem and the principle of maximum entropy 2009 Advanced Mathematical & Computational Tools in Metrology VIII 213 8.42, Unsicherheit F. Pavese, M. Bär, J.M. Limares, C. Perruchet, N.F. Zhang World Scientific New Jersey Series on Advances in Mathematics for Applied Sciences 78 31 ILira CElster WWöger M GCox inbook Wuebbeler2009 Impact of correlation in the measured frequency response on the results of a dynamic calibration 2009 Advanced Mathematical & Computational Tools in Metrology VIII 369-374 dynamic measurement, frequency response, dynamic calibration 8.42, Dynamik, Unsicherheit F. Pavese, M. Bär, J.M. Limares, C. Perruchet, N.F. Zhang World Scientific New Jersey Series on Advances in Mathematics for Applied Sciences 78 52 GWübbeler ALink TBruns CElster inbook Elster2009m Analysis of dynamic measurements: compensation of dynamic error and evaluation of uncertainty 2009 Advanced Mathematical & Computational Tools in Metrology VIII 80-89 8.42, Dynamik, Unsicherheit F. Pavese, M. Bär, J.M. Limares, C. Perruchet, N.F. Zhang World Scientific New Jersey Series on Advances in Mathematics for Applied Sciences 78 13 CElster ALink article Wubbeler2008 Evaluation of measurement uncertainty and its numerical calculation by a Monte Carlo method Measurement Science and Technology 2008 19 8 084009 8.42,Unsicherheit http://iopscience.iop.org/article/10.1088/0957-0233/19/8/084009 IOP Publishing en 0957-0233 10.1088/0957-0233/19/8/084009 GWübbeler MKrystek CElster article Elster2008c Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system Metrologia 2008 45 4 464-473 dynamic measurement, digital filter, deconvolution, dynamic uncertainty 8.42,Dynamik, Unsicherheit IOP Publishing 10.1088/0026-1394/45/4/013 CElster ALink article Lira2007 Probabilistic and least-squares inference of the parameters of a straight-line model Metrologia 2007 44 5 379--384 8.42,Bayes,Regression,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/44/5/014 IOP Publishing en 0026-1394 10.1088/0026-1394/44/5/014 ILira CElster WWöger article Elster2007 Draft GUM Supplement 1 and Bayesian analysis Metrologia 2007 44 3 L31--L32 8.42,Bayes,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/44/3/N03 IOP Publishing en 0026-1394 10.1088/0026-1394/44/3/N03 CElster WWöger M GCox article Elster2007b Analysis of dynamic measurements and determination of time-dependent measurement uncertainty using a second-order model Measurement Science and Technology 2007 18 12 3682-3687 dynamic measurement 8.42,Dynamik, Unsicherheit IOP Publishing en 10.1088/0957-0233/18/12/002 CElster ALink TBruns article Elster2007a Calculation of uncertainty in the presence of prior knowledge Metrologia 2007 44 2 111--116 8.42,Unsicherheit http://iopscience.iop.org/article/10.1088/0026-1394/44/2/002 IOP Publishing en 0026-1394 10.1088/0026-1394/44/2/002 CElster article Elster2005a Quantitative magnetic resonance spectroscopy: semi-parametric modeling and determination of uncertainties Magnetic resonance in medicine 2005 53 6 1288--96 A semi-parametric approach for the quantitative analysis of magnetic resonance (MR) spectra is proposed and an uncertainty analysis is given. Single resonances are described by parametric models or by parametrized in vitro spectra and the baseline is determined nonparametrically by regularization. By viewing baseline estimation in a reproducing kernel Hilbert space, an explicit parametric solution for the baseline is derived. A Bayesian point of view is adopted to derive uncertainties, and the many parameters associated with the baseline solution are treated as nuisance parameters. The derived uncertainties formally reduce to Cram<prt>é</prt>r-Rao lower bounds for the parametric part of the model in the case of a vanishing baseline. The proposed uncertainty calculation was applied to simulated and measured MR spectra and the results were compared to Cram<prt>é</prt>r-Rao lower bounds derived after the nonparametrically estimated baselines were subtracted from the spectra. In particular, for high SNR and strong baseline contributions the proposed procedure yields a more appropriate characterization of the accuracy of parameter estimates than Cr<prt>é</prt>mer-Rao lower bounds, which tend to overestimate accuracy. Bayes Theorem,Brain Chemistry,Computer Simulation,Computer-Assisted,Humans,Least-Squares Analysis,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: methods,Models, Statistical,Regression,Signal Processing, Computer-Assisted,Statistical 8.42, Unsicherheit, in-vivo http://www.ncbi.nlm.nih.gov/pubmed/15906296 0740-3194 10.1002/mrm.20500 CElster FSchubert ALink MWalzel FSeifert HRinneberg