3,971 research outputs found
The Theory and Practice of Estimating the Accuracy of Dynamic Flight-Determined Coefficients
Means of assessing the accuracy of maximum likelihood parameter estimates obtained from dynamic flight data are discussed. The most commonly used analytical predictors of accuracy are derived and compared from both statistical and simplified geometrics standpoints. The accuracy predictions are evaluated with real and simulated data, with an emphasis on practical considerations, such as modeling error. Improved computations of the Cramer-Rao bound to correct large discrepancies due to colored noise and modeling error are presented. The corrected Cramer-Rao bound is shown to be the best available analytical predictor of accuracy, and several practical examples of the use of the Cramer-Rao bound are given. Engineering judgement, aided by such analytical tools, is the final arbiter of accuracy estimation
Explicit formula for the Holevo bound for two-parameter qubit estimation problem
The main contribution of this paper is to derive an explicit expression for
the fundamental precision bound, the Holevo bound, for estimating any
two-parameter family of qubit mixed-states in terms of quantum versions of
Fisher information. The obtained formula depends solely on the symmetric
logarithmic derivative (SLD), the right logarithmic derivative (RLD) Fisher
information, and a given weight matrix. This result immediately provides
necessary and sufficient conditions for the following two important classes of
quantum statistical models; the Holevo bound coincides with the SLD Cramer-Rao
bound and it does with the RLD Cramer-Rao bound. One of the important results
of this paper is that a general model other than these two special cases
exhibits an unexpected property: The structure of the Holevo bound changes
smoothly when the weight matrix varies. In particular, it always coincides with
the RLD Cramer-Rao bound for a certain choice of the weight matrix. Several
examples illustrate these findings.Comment: 20 pages, 3 figures; to appear in J. Math. Phy
On Limits of Performance of DNA Microarrays
DNA microarray technology relies on the hybridization process which is stochastic in nature. Probabilistic cross-hybridization of non-specific targets, as well as the shot-noise originating from specific targets binding, are among the many obstacles for achieving high accuracy in DNA microarray analysis. In this paper, we use statistical model of hybridization and cross-hybridization processes to derive a lower bound (viz., the Cramer-Rao bound) on the minimum mean-square error of the target concentrations estimation. A preliminary study of the Cramer-Rao bound for estimating the target concentrations suggests that, in some regimes, cross-hybridization may, in fact, be beneficial—a result with potential ramifications for probe design, which is currently focused on minimizing cross-hybridization
Performance analysis of the Least-Squares estimator in Astrometry
We characterize the performance of the widely-used least-squares estimator in
astrometry in terms of a comparison with the Cramer-Rao lower variance bound.
In this inference context the performance of the least-squares estimator does
not offer a closed-form expression, but a new result is presented (Theorem 1)
where both the bias and the mean-square-error of the least-squares estimator
are bounded and approximated analytically, in the latter case in terms of a
nominal value and an interval around it. From the predicted nominal value we
analyze how efficient is the least-squares estimator in comparison with the
minimum variance Cramer-Rao bound. Based on our results, we show that, for the
high signal-to-noise ratio regime, the performance of the least-squares
estimator is significantly poorer than the Cramer-Rao bound, and we
characterize this gap analytically. On the positive side, we show that for the
challenging low signal-to-noise regime (attributed to either a weak
astronomical signal or a noise-dominated condition) the least-squares estimator
is near optimal, as its performance asymptotically approaches the Cramer-Rao
bound. However, we also demonstrate that, in general, there is no unbiased
estimator for the astrometric position that can precisely reach the Cramer-Rao
bound. We validate our theoretical analysis through simulated digital-detector
observations under typical observing conditions. We show that the nominal value
for the mean-square-error of the least-squares estimator (obtained from our
theorem) can be used as a benchmark indicator of the expected statistical
performance of the least-squares method under a wide range of conditions. Our
results are valid for an idealized linear (one-dimensional) array detector
where intra-pixel response changes are neglected, and where flat-fielding is
achieved with very high accuracy.Comment: 35 pages, 8 figures. Accepted for publication by PAS
Optimization of fringe-type laser anemometers for turbine engine component testing
The fringe type laser anemometer is analyzed using the Cramer-Rao bound for the variance of the estimate of the Doppler frequency as a figure of merit. Mie scattering theory is used to calculate the Doppler signal wherein both the amplitude and phase of the scattered light are taken into account. The noise from wall scatter is calculated using the wall bidirectional reflectivity and the irradiance of the incident beams. A procedure is described to determine the optimum aperture mask for the probe volume located a given distance from a wall. The expected performance of counter type processors is also discussed in relation to the Cramer-Rao bound. Numerical examples are presented for a coaxial backscatter anemometer
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