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bib
--- Timezone: CEST
Creation date: 2024-04-24
Creation time: 12-47-57
--- Number of references
32
article
KastnerMHMPCWHRE2023
Compressed AFM-IR hyperspectral nanoimaging
Measurement Science and Technology
2023
9
21
8.4,8.42,LargeScaleDataAna,Regression
accepted
10.1088/1361-6501/acfc27
BKästner
MMarschall
AHornemann
SMetzner
PPatoka
SCortes
GWübbeler
AHoehl
ERühl
CElster
article
WubbelerMRKE2021
Compressive nano-FTIR chemical mapping
Measurement Science and Technology
2021
12
24
33
035402
8.4,8.42,LargeScaleDataAna,Regression
accepted
10.1088/1361-6501/ac407a
GWübbeler
MMarschall
ERühl
BKästner
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
KlauenbergMBCvE2021
The GUM perspective on straight-line errors-in-variables regression
Measurement
2021
11
6
187
110340
8.4,8.42,Regression
0263-2241
10.1016/j.measurement.2021.110340
KKlauenberg
SMartens
ABošnjaković
M.GCox
A. M.Hvan der Veen
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
WubbelerE2020_2
Efficient experimental sampling through low-rank matrix recovery
Metrologia
2021
1
7
58
1
014002
online
8.4,8.42,Regression,LargeScaleDataAna
accepted
10.1088/1681-7575/abc97b
GWübbeler
CElster
article
MarschallHWHRKE2020
Compressed FTIR spectroscopy using low-rank matrix reconstruction
Opt. Express
2020
12
10
26
28
38762--38772
8.4,8.42,Regression,LargeScaleDataAna
10.1364/OE.404959
MMarschall
AHornemann
GWübbeler
AHoehl
ERühl
BKästner
CElster
techreport
MartensKE2020
Calibration of a torque measuring system – GUM uncertainty evaluation for least-squares versus Bayesian inference
2020
11
15
8.4,8.42,Regression
http://empir.npl.co.uk/emue/wp-content/uploads/sites/49/2020/12/Compendium_M27.pdf
Adriaan M.H. van der Veen, Maurice G. Cox
Teddington, United Kingdom
M27
E14
Good Practice in Evaluating Measurement uncertainty - Compendium of examples
SMartens
KKlauenberg
CElster
techreport
MartensKNCEE2020
Quantifying uncertainty when comparing measurement methods – Haemoglobin concentration as an example of correlation in straight-line regression
2020
11
15
8.4,8.42,Regression
http://empir.npl.co.uk/emue/wp-content/uploads/sites/49/2020/12/Compendium_M27.pdf
Adriaan M.H. van der Veen, Maurice G. Cox
Teddington, United Kingdom
M27
E13
Good Practice in Evaluating Measurement uncertainty - Compendium of examples
SMartens
KKlauenberg
JNeukammer
SCowen
S L REllison
CElster
techreport
MartensKMYFE2020
Calibration of a sonic nozzle as an example for quantifying all uncertainties involved in straight-line regression
2020
11
15
8.4,8.42,Regression
http://empir.npl.co.uk/emue/wp-content/uploads/sites/49/2020/12/Compendium_M27.pdf
Adriaan M.H. van der Veen, Maurice G. Cox
Teddington, United Kingdom
M27
E11
Good Practice in Evaluating Measurement uncertainty - Compendium of examples
SMartens
KKlauenberg
BMickan
CYardin
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
LehnertKWCSE2019
Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging
SIAM J. Imaging Sci.
2019
12
12
12
4
2035--2062
8.4,8.42,Regression,LargeScaleDataAna
10.1137/19M1246274
JudithLehnert
ChristophKolbitsch
GerdWübbeler
AmedeoChiribiri
TobiasSchäffter
ClemensElster
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
LehnertWKCCESSE2018
Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization
Physics in Medicine & Biology
2018
10
10
63
215017
8.4, 8.42,Regression,LargeScaleDataAna
10.1088/1361-6560/aae758
JLehnert
GWübbeler
CKolbitsch
AChiribiri
LCoquelin
GEbrard
NSmith
TSchäffter
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
ElsterW2017
Bayesian inference using a noninformative prior for linear Gaussian random coefficient regression with inhomogeneous within-class variances
Comput. Stat.
2017
1
3
32
1
51--69
A Bayesian inference for a linear Gaussian random coefficient regression model with inhomogeneous within-class variances is presented. The model is moti- vated by an application in metrology, but it may well find interest in other fields. We consider the selection of a noninformative prior for the Bayesian inference to address applications where the available prior knowledge is either vague or shall be ignored. The noninformative prior is derived by applying the Berger and Bernardo reference prior principle with the means of the random coefficients forming the parameters of interest. We show that the resulting posterior is proper and specify conditions for the existence of first and second moments of the marginal posterior. Simulation results are presented which suggest good frequentist properties of the proposed inference. The calibration of sonic nozzle data is considered as an application from metrology. The proposed inference is applied to these data and the results are compared to those obtained by alternative approaches.
random coefficient regression, Bayesian inference, noninformative prior
8.42, Regression
10.1007/s00180-015-0641-3
CElster
GWübbeler
article
DierlEFKEE2016
Improved estimation of reflectance spectra by utilizing prior knowledge
Journal of the Optical Society of America A
2016
6
23
33
7
1370--1376
dynamic measurement, dynamic uncertainty, deconvolution
8.42, Dynamik, Regression
10.1364/JOSAA.33.001370
MDierl
TEckhard
BFrei
MKlammer
SEichstädt
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
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
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
Klauenberg2015
Informative prior distributions for ELISA analyses
Biostatistics
2015
1
1
16
3
454--64
Immunoassays are capable of measuring very small concentrations of substances in solutions and have an immense range of application. Enzyme-linked immunosorbent assay (ELISA) tests in particular can detect the presence of an infection, of drugs, or hormones (as in the home pregnancy test). Inference of an unknown concentration via ELISA usually involves a non-linear heteroscedastic regression and subsequent prediction, which can be carried out in a Bayesian framework. For such a Bayesian inference, we are developing informative prior distributions based on extensive historical ELISA tests as well as theoretical considerations. One consideration regards the quality of the immunoassay leading to two practical requirements for the applicability of the priors. Simulations show that the additional prior information can lead to inferences which are robust to reasonable perturbations of the model and changes in the design of the data. On real data, the applicability is demonstrated across different laboratories, for different analytes and laboratory equipment as well as for previous and current ELISAs with sigmoid regression function. Consistency checks on real data (similar to cross-validation) underpin the adequacy of the suggested priors. Altogether, the new priors may improve concentration estimation for ELISAs that fulfill certain design conditions, by extending the range of the analyses, decreasing the uncertainty, or giving more robust estimates. Future use of these priors is straightforward because explicit, closed-form expressions are provided. This work encourages development and application of informative, yet general, prior distributions for other types of immunoassays.
Regression, 8.42, ELISA
http://biostatistics.oxfordjournals.org/content/16/3/454
1468-4357
10.1093/biostatistics/kxu057
KKlauenberg
MWalzel
BEbert
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
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
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
Eichstadt2014a
Reliable uncertainty evaluation for ODE parameter estimation - a comparison
Journal of Physics: Conference Series
2014
490
1
012230
Regression, ODE, parameter identification, dynamic calibration, modelling
8.42,Dynamik, Regression
http://iopscience.iop.org/article/10.1088/1742-6596/490/1/012230
IOP Publishing
en
1742-6596
10.1088/1742-6596/490/1/012230
SEichstädt
CElster
article
Heidenreich2014a
A surrogate model enables a Bayesian approach to the inverse problem of scatterometry
J. Phys. Conf. Ser.
2014
490
1
012007
8.43,Bayes,Scatter-Inv,Regression,8.42, UQ
http://iopscience.iop.org/article/10.1088/1742-6596/490/1/012007
IOP Publishing
en
1742-6596
10.1088/1742-6596/490/1/012007
SHeidenreich
HGross
M-AHenn
CElster
MBär
article
Matthews2014e
Mathematical modelling to support traceable dynamic calibration of pressure sensors
Metrologia
2014
51
3
326-338
dynamic measurement, pressure, parametric model
8.42, Dynamik, Regression
fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/Mathematical_Modelling_Dynamic_Pressure_preprint.pdf
http://iopscience.iop.org/article/10.1088/0026-1394/51/3/326
IOP Publishing
en
doi:10.1088/0026-1394/51/3/326
0026-1394
10.1088/0026-1394/51/3/326
CMatthews
FPennecchi
SEichstädt
AMalengo
TEsward
I MSmith
CElster
AKnott
FArrhén
ALakka
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
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
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
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