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2007b). Tuning parameter selectors for the smoothly clipped absolute deviation method
 Biometrika
"... The penalised least squares approach with smoothly clipped absolute deviation penalty has been consistently demonstrated to be an attractive regression shrinkage and selection method. It not only automatically and consistently selects the important variables, but also produces estimators which are ..."
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Cited by 71 (9 self)
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The penalised least squares approach with smoothly clipped absolute deviation penalty has been consistently demonstrated to be an attractive regression shrinkage and selection method. It not only automatically and consistently selects the important variables, but also produces estimators which
Weighted Wilcoxontype Smoothly Clipped Absolute Deviation Method
"... Summary: Shrinkagetype variable selection procedures have recently seen increasing applications in biomedical research. However, their performance can be adversely influenced by outliers in either the response or the covariate space. This paper proposes a weighted Wilcoxontype smoothly clipped abs ..."
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Cited by 2 (1 self)
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absolute deviation (WWSCAD) method, which deals with robust variable selection and robust estimation simultaneously. The new procedure can be conveniently implemented with the statistical software R. We establish that the WWSCAD correctly identifies the set of zero coefficients with probability
Supplementary Material for “Weighted Wilcoxontype Smoothly Clipped Absolute Deviation Method ” by Lan Wang and Runze Li
, 2008
"... We use the following notation in the proofs: Qn(β) = n −1∑ i<j bijei − ej+ n d∑ j=1 p′λ(β0j )βj Dn(β) = n −1∑ i<j bijei − ej Sn(β) = n −1∑ i<j bij(xi − xj)sgn((Yi − Yj) − (xi − xj)′β) An(β) = (2 3τ)−1(β − β0)′X′WX(β − β0) − (β − β0)′Sn(β0) +Dn(β0), where sgn(x) stands for the si ..."
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We use the following notation in the proofs: Qn(β) = n −1∑ i<j bijei − ej+ n d∑ j=1 p′λ(β0j )βj Dn(β) = n −1∑ i<j bijei − ej Sn(β) = n −1∑ i<j bij(xi − xj)sgn((Yi − Yj) − (xi − xj)′β) An(β) = (2 3τ)−1(β − β0)′X′WX(β − β0) − (β − β0)′Sn(β0) +Dn(β0), where sgn(x) stands for the sign of x. We first present and prove two useful lemmas about the unpenalized weightedWilcoxon estimator under possible local contamination. These results will be useful later to establish the asymptotic properties of the penalized Wilcoxon estimator. In the proof of the two lemmas, we frequently refer to the book of Hettmansperger and McKean (1998), abbreviated as HM in the sequel. Lemma 0.1 Assume the regularity conditions in Section 3.1, then ∀ > 0, ∀c> 0, sup√ nβ−β0≤c Dn(β) − An(β)  ≥ p → 0 (1) under either H or H∗n. Proof. The result under H was given in Sievers (1983), see also Section 5.2 of HM. To prove it underH∗n, let Un(t) = n −1/2[Sn(β0+t/
Closedform solution of absolute orientation using unit quaternions
 J. Opt. Soc. Am. A
, 1987
"... Finding the relationship between two coordinate systems using pairs of measurements of the coordinates of a number of points in both systems is a classic photogrammetric task. It finds applications in stereophotogrammetry and in robotics. I present here a closedform solution to the leastsquares pr ..."
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Cited by 973 (4 self)
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squares problem for three or more points. Currently various empirical, graphical, and numerical iterative methods are in use. Derivation of the solution is simplified by use of unit quaternions to represent rotation. I emphasize a symmetry property that a solution to this problem ought to possess. The best
Least absolute deviations estimation for the censored regression model
 Journal of Econometrics
, 1984
"... This paper proposes an alternative to maximum likelihood estimation of the parameters of the censored regression (or censored ‘Tobit’) model. The proposed estimator is a generalization of least absolute deviations estimation for the standard linear model, and, unlike estimation methods based on the ..."
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Cited by 277 (6 self)
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This paper proposes an alternative to maximum likelihood estimation of the parameters of the censored regression (or censored ‘Tobit’) model. The proposed estimator is a generalization of least absolute deviations estimation for the standard linear model, and, unlike estimation methods based
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 951 (12 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
Image registration methods: a survey
 IMAGE AND VISION COMPUTING
, 2003
"... This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align t ..."
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Cited by 734 (9 self)
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This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align
An introduction to variational methods for graphical models
 TO APPEAR: M. I. JORDAN, (ED.), LEARNING IN GRAPHICAL MODELS
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