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692,622
Covariance shaping leastsquares estimation
 IEEE Trans. Signal Process
, 2003
"... Abstractâ€”A new linear estimator is proposed, which we refer to as the covariance shaping leastsquares (CSLS) estimator, for estimating a set of unknown deterministic parameters x observed through a known linear transformation H and corrupted by additive noise. The CSLS estimator is a biased estimat ..."
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Cited by 31 (17 self)
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Abstractâ€”A new linear estimator is proposed, which we refer to as the covariance shaping leastsquares (CSLS) estimator, for estimating a set of unknown deterministic parameters x observed through a known linear transformation H and corrupted by additive noise. The CSLS estimator is a biased
LeastSquares Policy Iteration
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach ..."
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Cited by 461 (12 self)
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We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach
Least Median of Squares Regression
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 1984
"... ..."
Efficient Estimation Algorithm for Speech Signal
"... In this paper, a modified estimation algorithm has been developed refers to covariance shaping least square estimation based on the quantum mechanical concepts and constraints. The algorithm has been applied to the speech signal and the performance is estimated using probability theories. The same m ..."
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In this paper, a modified estimation algorithm has been developed refers to covariance shaping least square estimation based on the quantum mechanical concepts and constraints. The algorithm has been applied to the speech signal and the performance is estimated using probability theories. The same
Covariance shaping approach to linear leastsquares estimation
 in Proc. Asilomar Conf. Signals, Systems, and Computers,Nov
, 2002
"... A new biased linear estimator, referred to as the covariance shaping leastsquares (CSLS) estimator, is proposed for estimating a set of unknown deterministic parameters in a linear model. The CSLS estimator is directed at improving the performance of the leastsquares (LS) estimator by choosing the ..."
Abstract

Cited by 1 (1 self)
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A new biased linear estimator, referred to as the covariance shaping leastsquares (CSLS) estimator, is proposed for estimating a set of unknown deterministic parameters in a linear model. The CSLS estimator is directed at improving the performance of the leastsquares (LS) estimator by choosing
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
 ACM Trans. Math. Software
, 1982
"... An iterative method is given for solving Ax ~ffi b and minU Ax b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable numerica ..."
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Cited by 649 (21 self)
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gradient algorithms, indicating that I~QR is the most reliable algorithm when A is illconditioned. Categories and Subject Descriptors: G.1.2 [Numerical Analysis]: ApprorJmationleast squares approximation; G.1.3 [Numerical Analysis]: Numerical Linear Algebralinear systems (direct and
A HeteroskedasticityConsistent Covariance Matrix Estimator And A Direct Test For Heteroskedasticity
, 1980
"... This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator ..."
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Cited by 3060 (5 self)
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to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation
Directional Statistics and Shape Analysis
, 1995
"... There have been various developments in shape analysis in the last decade. We describe here some relationships of shape analysis with directional statistics. For shape, rotations are to be integrated out or to be optimized over whilst they are the basis for directional statistics. However, various c ..."
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Cited by 775 (31 self)
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There have been various developments in shape analysis in the last decade. We describe here some relationships of shape analysis with directional statistics. For shape, rotations are to be integrated out or to be optimized over whilst they are the basis for directional statistics. However, various
Shape Matching and Object Recognition Using Shape Contexts
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform ..."
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Cited by 1787 (21 self)
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We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning
NORMALISED LEASTSQUARES ESTIMATION IN . . .
, 2006
"... We investigate the timevarying ARCH (tvARCH) process. It is shown that it can be used to describe the slow decay of the sample autocorrelations of the squared returns often observed in financial time series, which warrants the further study of parameter estimation methods for the model. Since the p ..."
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the parameters are changing over time, a successful estimator needs to perform well for small samples. We propose a kernel normalisedleastsquares (kernelNLS) estimator which has a closed form, and thus outperforms the previously proposed kernel quasimaximum likelihood (kernelQML) estimator for small
Results 1  10
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692,622