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Parametric statistics
About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.
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TL;DR: A new semiparametric/sparse method is introduced, called SPICE, which is computationally quite efficient, enjoys global convergence properties, can be readily used in the case of replicated measurements and, unlike most other sparse estimation methods, does not require any subtle choices of user parameters.
Abstract: Separable models occur frequently in spectral analysis, array processing, radar imaging and astronomy applications Statistical inference methods for these models can be categorized in three large classes: parametric, nonparametric (also called “dense”) and semiparametric (also called “sparse”) We begin by discussing the advantages and disadvantages of each class Then we go on to introduce a new semiparametric/sparse method called SPICE (a semiparametric/sparse iterative covariance-based estimation method) SPICE is computationally quite efficient, enjoys global convergence properties, can be readily used in the case of replicated measurements and, unlike most other sparse estimation methods, does not require any subtle choices of user parameters We illustrate the statistical performance of SPICE by means of a line-spectrum estimation study for irregularly sampled data
228 citations
TL;DR: Two provably convergent algorithms are proposed to obtain suboptimal solutions of the spectral efficiency of full-duplex small cell wireless systems by approximate the design problem by a determinant maximization program in each iteration of the first algorithm.
Abstract: We investigate the spectral efficiency of full-duplex small cell wireless systems, in which a full-duplex capable base station (BS) is designed to send/receive data to/from multiple half-duplex users on the same system resources. The major hurdle for designing such systems is due to the self-interference at the BS and co-channel interference among users. Hence, we consider a joint beamformer design to maximize the spectral efficiency subject to certain power constraints. The design problem is first formulated as a rank-constrained optimization problem, and the rank relaxation method is then applied. However, the relaxed problem is still nonconvex, and thus, optimal solutions are hard to find. Herein, we propose two provably convergent algorithms to obtain suboptimal solutions. Based on the concept of the Frank-Wolfe algorithm, we approximate the design problem by a determinant maximization program in each iteration of the first algorithm. The second method is built upon the sequential parametric convex approximation method, which allows us to transform the relaxed problem into a semidefinite program in each iteration. Extensive numerical experiments under small cell setups illustrate that the full-duplex system with the proposed algorithms can achieve a large gain over the half-duplex system.
228 citations
TL;DR: In this article, a general non-parametric probabilistic approach of model uncertainties for dynamical systems has been proposed using the random matrix theory, and a comprehensive overview of this approach in developing its foundations in simple terms and illustrating all the concepts and the tools introduced in the general theory, by using a simple example.
226 citations
TL;DR: A new method which requires a small set of measurements of a simple calibration object consisting of two spherical objects, that can be considered as 'point' objects, which can be determined analytically using explicit formulae.
Abstract: This paper is about calibration of cone-beam (CB) scanners for both x-ray computed tomography and single-photon emission computed tomography. Scanner calibration refers here to the estimation of a set of parameters which fully describe the geometry of data acquisition. Such parameters are needed for the tomographic reconstruction step. The discussion is limited to the usual case where the cone vertex and planar detector move along a circular path relative to the object. It is also assumed that the detector does not have spatial distortions. We propose a new method which requires a small set of measurements of a simple calibration object consisting of two spherical objects, that can be considered as 'point' objects. This object traces two ellipses on the detector and from the parametric description of these ellipses, the calibration geometry can be determined analytically using explicit formulae. The method is robust and easy to implement. However, it is not fully general as it is assumed that the detector is parallel to the rotation axis of the scanner. Implementation details are given for an experimental x-ray CB scanner.
226 citations
01 Jan 2007
TL;DR: In this article, the authors present an approach to the construction of lower bounds for the coercivity and inf-sup stability constants required in a posteriori error analysis of reduced basis approximations to affinely parametrized partial differential equations.
Abstract: We present an approach to the construction of lower bounds for the coercivity and inf–sup stability constants required in a posteriori error analysis of reduced basis approximations to affinely parametrized partial differential equations. The method, based on an Offline–Online strategy relevant in the reduced basis many-query and real-time context, reduces the Online calculation to a small Linear Program: the objective is a parametric expansion of the underlying Rayleigh quotient; the constraints reflect stability information at optimally selected parameter points. Numerical results are presented for coercive elasticity and non-coercive
226 citations