2,133 research outputs found
On the non-existence of unbiased estimators in constrained estimation problems
We address the problem of existence of unbiased constrained parameter estimators. We show that if the constrained set of parameters is compact and the hypothesized distributions are absolutely continuous with respect to one another, then there exists no unbiased estimator.Weaker conditions for the absence of unbiased constrained estimators are also specified. We provide several examples which demonstrate the utility of these conditions
On the non-existence of unbiased estimators in constrained estimation problems
We address the problem of existence of unbiased constrained parameter estimators. We show that if the constrained set of parameters is compact and the hypothesized distributions are absolutely continuous with respect to one another, then there exists no unbiased estimator.Weaker conditions for the absence of unbiased constrained estimators are also specified. We provide several examples which demonstrate the utility of these conditions
Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental findings and applications
Inferring information from a set of acquired data is the main objective of
any signal processing (SP) method. In particular, the common problem of
estimating the value of a vector of parameters from a set of noisy measurements
is at the core of a plethora of scientific and technological advances in the
last decades; for example, wireless communications, radar and sonar,
biomedicine, image processing, and seismology, just to name a few. Developing
an estimation algorithm often begins by assuming a statistical model for the
measured data, i.e. a probability density function (pdf) which if correct,
fully characterizes the behaviour of the collected data/measurements.
Experience with real data, however, often exposes the limitations of any
assumed data model since modelling errors at some level are always present.
Consequently, the true data model and the model assumed to derive the
estimation algorithm could differ. When this happens, the model is said to be
mismatched or misspecified. Therefore, understanding the possible performance
loss or regret that an estimation algorithm could experience under model
misspecification is of crucial importance for any SP practitioner. Further,
understanding the limits on the performance of any estimator subject to model
misspecification is of practical interest. Motivated by the widespread and
practical need to assess the performance of a mismatched estimator, the goal of
this paper is to help to bring attention to the main theoretical findings on
estimation theory, and in particular on lower bounds under model
misspecification, that have been published in the statistical and econometrical
literature in the last fifty years. Secondly, some applications are discussed
to illustrate the broad range of areas and problems to which this framework
extends, and consequently the numerous opportunities available for SP
researchers.Comment: To appear in the IEEE Signal Processing Magazin
Spectral unmixing of Multispectral Lidar signals
In this paper, we present a Bayesian approach for spectral unmixing of
multispectral Lidar (MSL) data associated with surface reflection from targeted
surfaces composed of several known materials. The problem addressed is the
estimation of the positions and area distribution of each material. In the
Bayesian framework, appropriate prior distributions are assigned to the unknown
model parameters and a Markov chain Monte Carlo method is used to sample the
resulting posterior distribution. The performance of the proposed algorithm is
evaluated using synthetic MSL signals, for which single and multi-layered
models are derived. To evaluate the expected estimation performance associated
with MSL signal analysis, a Cramer-Rao lower bound associated with model
considered is also derived, and compared with the experimental data. Both the
theoretical lower bound and the experimental analysis will be of primary
assistance in future instrument design
Optimal experiment design revisited: fair, precise and minimal tomography
Given an experimental set-up and a fixed number of measurements, how should
one take data in order to optimally reconstruct the state of a quantum system?
The problem of optimal experiment design (OED) for quantum state tomography was
first broached by Kosut et al. [arXiv:quant-ph/0411093v1]. Here we provide
efficient numerical algorithms for finding the optimal design, and analytic
results for the case of 'minimal tomography'. We also introduce the average
OED, which is independent of the state to be reconstructed, and the optimal
design for tomography (ODT), which minimizes tomographic bias. We find that
these two designs are generally similar. Monte-Carlo simulations confirm the
utility of our results for qubits. Finally, we adapt our approach to deal with
constrained techniques such as maximum likelihood estimation. We find that
these are less amenable to optimization than cruder reconstruction methods,
such as linear inversion.Comment: 16 pages, 7 figure
Partial Relaxation Approach: An Eigenvalue-Based DOA Estimator Framework
In this paper, the partial relaxation approach is introduced and applied to
DOA estimation using spectral search. Unlike existing methods like Capon or
MUSIC which can be considered as single source approximations of multi-source
estimation criteria, the proposed approach accounts for the existence of
multiple sources. At each considered direction, the manifold structure of the
remaining interfering signals impinging on the sensor array is relaxed, which
results in closed form estimates for the interference parameters. The
conventional multidimensional optimization problem reduces, thanks to this
relaxation, to a simple spectral search. Following this principle, we propose
estimators based on the Deterministic Maximum Likelihood, Weighted Subspace
Fitting and covariance fitting methods. To calculate the pseudo-spectra
efficiently, an iterative rooting scheme based on the rational function
approximation is applied to the partial relaxation methods. Simulation results
show that the performance of the proposed estimators is superior to the
conventional methods especially in the case of low Signal-to-Noise-Ratio and
low number of snapshots, irrespectively of any specific structure of the sensor
array while maintaining a comparable computational cost as MUSIC.Comment: This work has been submitted to IEEE for possible publication.
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Primordial non-Gaussianity and Bispectrum Measurements in the Cosmic Microwave Background and Large-Scale Structure
The most direct probe of non-Gaussian initial conditions has come from
bispectrum measurements of temperature fluctuations in the Cosmic Microwave
Background and of the matter and galaxy distribution at large scales. Such
bispectrum estimators are expected to continue to provide the best constraints
on the non-Gaussian parameters in future observations. We review and compare
the theoretical and observational problems, current results and future
prospects for the detection of a non-vanishing primordial component in the
bispectrum of the Cosmic Microwave Background and large-scale structure, and
the relation to specific predictions from different inflationary models.Comment: 82 pages, 23 figures; Invited Review for the special issue "Testing
the Gaussianity and Statistical Isotropy of the Universe" for Advances in
Astronom
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