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166,658
Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11972 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
Recent advances in least squares 3D surface matching
 OPTICAL 3D MEASUREMENT TECHNIQUES VII
, 2005
"... We present an algorithm for the least squares matching of overlapping 3D surfaces. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formula ..."
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Cited by 3 (1 self)
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We present an algorithm for the least squares matching of overlapping 3D surfaces. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces
LEAST SQUARES MATCHING OF 3D SURFACES
"... An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters of one or more fully 3D surfaces with respect to a template one, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between ..."
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An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters of one or more fully 3D surfaces with respect to a template one, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances
SIMULTANEOUS COREGISTRATION AND GEOREFERENCING OF MULTIPLE POINTCLOUDS
"... ABSTRACT: A method for the simultaneous coregistration and georeferencing of multiple 3D pointclouds and associated intensity information is proposed. It is a generalization of the 3D surface matching problem. The simultaneous coregistration provides for a strict solution to the problem, as oppose ..."
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, as opposed to sequential pairwise registration. The problem is formulated as the Least Squares matching of overlapping 3D surfaces. The parameters of 3D transformations of multiple surfaces are simultaneously estimated, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean
A flexible mathematical model for matching of 3D surfaces and attributes
 Videometrics VIII, Proc. of SPIEIS&T Electronic Imaging
"... An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formul ..."
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Cited by 3 (2 self)
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An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces
Fast correspondence search for 3D surface matching
 International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36 (Part 3/W19
, 2005
"... An algorithm for least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulatio ..."
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Cited by 12 (7 self)
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An algorithm for least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces
Registration of point clouds using range and intensity information
 PATERAKI AND M. BALTSAVIAS (EDS), INTERNATIONAL WORKSHOP ON RECORDING, MODELING AND VISUALIZATION OF CULTURAL HERITAGE
, 2005
"... An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formul ..."
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Cited by 6 (0 self)
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An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces
Longitudinal data analysis using generalized linear models”.
 Biometrika,
, 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
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Cited by 1526 (8 self)
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SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence
Generalized Autoregressive Conditional Heteroskedasticity
 JOURNAL OF ECONOMETRICS
, 1986
"... A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametri ..."
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Cited by 2406 (30 self)
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A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class
Results 1  10
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166,658