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Keywords
(17)
Approximation Method
Asymptotic Approximation
Asymptotic Distribution
Asymptotic Properties
Compton Scattering
Computer Model
Detection Threshold
Empirical Method
False Alarm Rate
Gamma Ray
Gammaray Sources
Hypothesis Test
Imaging System
Maximum Likelihood Estimate
Monte Carlo Simulation
Statistical Model
Maximum Likelihood
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Asymptotic Source Detection Performance of GammaRay Imaging Systems Under Model Mismatch
Asymptotic Source Detection Performance of GammaRay Imaging Systems Under Model Mismatch,10.1109/TSP.2011.2162326,IEEE Transactions on Signal Process
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Asymptotic Source Detection Performance of GammaRay Imaging Systems Under Model Mismatch
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Citations: 1
)
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Daniel J. Lingenfelter
,
Jeffrey A. Fessler
,
Clayton D. Scott
,
Zhong He
Likelihoodbased test statistics for the task of de tecting a radioactive source in background using a gammaray
imaging system
often have intractable distributions. This compli cates the tasks of predicting detection performance and setting thresholds that ensure desired falsealarm rates. Asymptotic distributions of test statistics can aid in predicting performance and in setting detection thresholds. However, in applications with complex sensors, like gammaray imaging, often only ap proximate statistical models for the measurements are available. Standard asymptotic approximations can yield inaccurate per formance predictions when based on misspecified models. This paper considers
asymptotic properties
of detection tests based on
maximum likelihood
(ML) estimates under model mismatch, i.e., when the
statistical model
used for detection differs from the true distribution. We provide general expressions for the
asymptotic distribution
of likelihoodbased test statistics when the number of measurements is Poisson, and expressions specific to gammaray source detection that one can evaluate using a modest amount of data from a real system or
Monte Carlo
simulation. Considering a simulated Compton imaging system, we show that the proposed expressions yield more accurate detection performance predic tions than previousexpressions that ignore modelmismatch.These expressions require less data and computation than conventional empirical methods.
Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 59, no. 11, pp. 51415151, 2011
DOI:
10.1109/TSP.2011.2162326
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Citation Context
(1)
...We apply the methodology of [
5
] to compute the detection performance in terms of ROC with an asymptotic approximation that accounts for model mismatch...
...Under suitable conditions, ˜ �� is the limit of the sequence of estimates ˜ �� �� as �� →∞ . One possible set of regularity conditions that guarantees existence, uniqueness, and convergence is given in [
5
]...
...The convergence of the QMLE is stated in Theorem 1, which extends Theorem 2.2 of [8] to the case of a Poisson number of measurements. The proof is given in [
5
]...
Daniel J. Lingenfelter
,
et al.
Predicting ROC curves for source detection under model mismatch
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Gammaray energyimaging integrated spectral deconvolution
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Journal:
Nuclear Instruments & Methods in Physics Research Section Aaccelerators Spectrometers Detectors and Associated Equipment  NUCL INSTRUM METH PHYS RES A
, vol. 574, no. 1, pp. 98109, 2007
Benefits of positionsensitive detectors for radioactive source detection
(
Citations: 4
)
Daniel J. Lingenfelter
,
Jeffrey A. Fessler
,
Clayton D. Scott
,
Zhong He
Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 58, no. 9, pp. 44734483, 2010
Maximum Likelihood Estimation of Misspecified Models
(
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)
Halbert White
Published in 1982.
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Citations
(1)
Predicting ROC curves for source detection under model mismatch
Daniel J. Lingenfelter
,
Jeffrey A. Fessler
,
Clayton D. Scott
,
Zhong He
Published in 2010.