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252,157
Recursive Estimation
 University of Copenhagen
, 1993
"... Summary: Iterated onestep Huberskip Mestimators are considered for regression problems. Each onestep estimator is a reweighted least squares estimators with zero/one weights determined by the initial estimator and the data. The asymptotic theory is given for iteration of such estimators using a ..."
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Cited by 7 (0 self)
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Summary: Iterated onestep Huberskip Mestimators are considered for regression problems. Each onestep estimator is a reweighted least squares estimators with zero/one weights determined by the initial estimator and the data. The asymptotic theory is given for iteration of such estimators using a
Recursive estimation of motion, structure, and focal length
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from motion ..."
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Cited by 300 (11 self)
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We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from
Distributed and Recursive Estimation
"... Abstract Estimation is a canonical problem in sensor networks. The intrinsic nature of sensor networks requires estimation algorithms based on sensor data to be distributed and recursive; such algorithms are studied in this chapter for the problem of (conditional) least squares estimation. The chapt ..."
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Abstract Estimation is a canonical problem in sensor networks. The intrinsic nature of sensor networks requires estimation algorithms based on sensor data to be distributed and recursive; such algorithms are studied in this chapter for the problem of (conditional) least squares estimation
Recursive estimation with implicit constraints
 Proceedings of the DAGM 2007, number 4713 in LNCS
, 2007
"... Abstract. Recursive estimation or Kalman filtering usually relies on explicit model functions, that directly and explicitly describe the effect of the parameters on the observations. However, many problems in computer vision, including all those resulting in homogeneous equation systems, are easier ..."
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Cited by 3 (2 self)
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Abstract. Recursive estimation or Kalman filtering usually relies on explicit model functions, that directly and explicitly describe the effect of the parameters on the observations. However, many problems in computer vision, including all those resulting in homogeneous equation systems, are easier
Recursive Estimation in Hidden Markov Models
 In Proc. CDC’97, IEEE Conference on Decision and Control
, 1997
"... We consider a hidden Markov model (HMM) with multidimensional observations, and where the coefficients (transition probability matrix, and observation conditional densities) depend on some unknown parameter. We study the asymptotic behaviour of two recursive estimators, the recursive maximum likelih ..."
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Cited by 18 (0 self)
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We consider a hidden Markov model (HMM) with multidimensional observations, and where the coefficients (transition probability matrix, and observation conditional densities) depend on some unknown parameter. We study the asymptotic behaviour of two recursive estimators, the recursive maximum
Optimal recursive estimation for discretetime descriptor systems
 International Journal of Systems Science
, 2005
"... Optimal recursive estimation for discretetime descriptor systems ..."
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Cited by 7 (0 self)
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Optimal recursive estimation for discretetime descriptor systems
Recursive estimation in econometrics
 Computational Statistics and Data Analysis
, 2003
"... An account is given of recursive regression and Kalman filtering that gathers the important results and the ideas that lie behind them. It emphasises areas where econometricians have made contributions, including methods for handling the initialvalue problem associated with nonstationary processes ..."
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Cited by 15 (3 self)
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An account is given of recursive regression and Kalman filtering that gathers the important results and the ideas that lie behind them. It emphasises areas where econometricians have made contributions, including methods for handling the initialvalue problem associated with nonstationary processes
On Fixed Gain Recursive Estimation Processes
, 1996
"... An important class of fixed gain recursive estimation processes can be approximated by random differential equations, the right hand sides of which are Lmixing random fields. It will be shown that the solution trajectories of these random differential equations follow the trajectories of the corres ..."
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Cited by 2 (1 self)
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An important class of fixed gain recursive estimation processes can be approximated by random differential equations, the right hand sides of which are Lmixing random fields. It will be shown that the solution trajectories of these random differential equations follow the trajectories
Results 1  10
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252,157