Results 1  10
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5,153
A Generalized Spatial Twostage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances
 Journal of Real Estate Finance and Economics
, 1998
"... Abstract Crosssectional spatial models frequently contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive. In this article we describe a computationally simple procedure for estimating crosssectional models that contain both of these c ..."
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Cited by 257 (21 self)
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Abstract Crosssectional spatial models frequently contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive. In this article we describe a computationally simple procedure for estimating crosssectional models that contain both
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 653 (21 self)
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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
Robust estimation of the vector autoregressive model by a least trimmed squares procedure
 COMPSTAT 2008: Proceedings in computational statistics
, 2008
"... Robust estimation of the vector autoregressive model by a trimmed least squares procedure ..."
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Cited by 5 (0 self)
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Robust estimation of the vector autoregressive model by a trimmed least squares procedure
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 476 (46 self)
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In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set
Unsupervised texture segmentation using Gabor filters
 Pattern Recognition
"... We presenf a texture segmentation algorithm inspired by the multichannel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatialfrequency domain. We propose a s ..."
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Cited by 616 (20 self)
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systematic filter selection scheme which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy ” in a window around each pixel. An unsupervised square
Greedy Function Approximation: A Gradient Boosting Machine
 Annals of Statistics
, 2000
"... Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additi ..."
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Cited by 1000 (13 self)
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for additive expansions based on any tting criterion. Specic algorithms are presented for least{squares, least{absolute{deviation, and Huber{M loss functions for regression, and multi{class logistic likelihood for classication. Special enhancements are derived for the particular case where the individual
Application of the Total Least Squares Procedure to Linear View Interpolation
, 1999
"... It is shown that, in comparison to the results obtained from a conventional least squares approach, a total least squares solution leads to significant improvements in the geometry and appearance of images synthesised in a linear combination of views procedure. Use of the total least squares criteri ..."
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Cited by 3 (0 self)
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It is shown that, in comparison to the results obtained from a conventional least squares approach, a total least squares solution leads to significant improvements in the geometry and appearance of images synthesised in a linear combination of views procedure. Use of the total least squares
Computation and Analysis of Multiple Structural Change Models.‖
 Journal of Applied Econometrics
, 2003
"... SUMMARY In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the pro ..."
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Cited by 448 (6 self)
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of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most leastsquares operations of order O T 2 for any number
A generalized spatial two stage least squares procedure for estimating a spatial autoregressive with autoregressive disturbances
 J. Real Estate Financ
, 1998
"... We would like to thank two anonymous referees for helpful comments. We assume, however, full responsibility for any shortcomings. 2 Department of Economics, University of Maryland, College Park ..."
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Cited by 59 (1 self)
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We would like to thank two anonymous referees for helpful comments. We assume, however, full responsibility for any shortcomings. 2 Department of Economics, University of Maryland, College Park
Sampling—50 years after Shannon
 Proceedings of the IEEE
, 2000
"... This paper presents an account of the current state of sampling, 50 years after Shannon’s formulation of the sampling theorem. The emphasis is on regular sampling, where the grid is uniform. This topic has benefited from a strong research revival during the past few years, thanks in part to the math ..."
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Cited by 339 (27 self)
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to the mathematical connections that were made with wavelet theory. To introduce the reader to the modern, Hilbertspace formulation, we reinterpret Shannon’s sampling procedure as an orthogonal projection onto the subspace of bandlimited functions. We then extend the standard sampling paradigm for a representation
Results 1  10
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5,153