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LSpline Wavelets
, 1994
"... We explicitly construct compactlysupported wavelets associated with Lspline spaces. We then apply the theory to develop multiresolution methods based on Lsplines. ..."
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Cited by 1 (0 self)
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We explicitly construct compactlysupported wavelets associated with Lspline spaces. We then apply the theory to develop multiresolution methods based on Lsplines.
Some Theory for LSpline Smoothing
, 1996
"... We consider the problem of estimating a smoothing spline where the penalty on the smoothing function g is R (Lg) 2 . An algorithm requiring O(n) operations where n is the number of values being smoothed is developed. The technique relies on results obtained by Anselone and Laurent (1968), and th ..."
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smoothing the data can be computed in a number of operations that is proportional to n or n log n;, that is, O(n) or O(n log n). The pathbreaking paper by Anselone and Laurent (1968), drawing on earlier unpublished work by Atteia (1966) and Greville (1964), dealt with spline smoothing in a very general
Generalized Lspline wavelet bases
 in Proceedings of the SPIE Conference on Mathematical Imaging: Wavelet XI
"... We build waveletlike functions based on a parametrized family of pseudodifferential operators L~ν that satisfy some admissibility and scalability conditions. The shifts of the generalized Bsplines, which are localized versions of the Green function of L~ν, generate a family of Lspline spaces. Th ..."
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Cited by 5 (4 self)
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We build waveletlike functions based on a parametrized family of pseudodifferential operators L~ν that satisfy some admissibility and scalability conditions. The shifts of the generalized Bsplines, which are localized versions of the Green function of L~ν, generate a family of Lspline spaces
Derivation Of Equivalent Kernel For General Spline Smoothing: A Systematic Approach
"... We consider first the spline smoothing nonparametric estimation with variable smoothing parameter and arbitrary design density function and show that the corresponding equivalent kernel can be approximated by the Green's function of a certain linear differential operator. Furthermore, we propo ..."
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Cited by 7 (0 self)
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case of the general solution. Then, we show how these ideas can be directly extended to the very general Lspline smoothing. 3 The author responsible for correspondence y Part of the work was done while the author was in the School of Mathematics, University of Bristol, UK 3 Key words and phrases
Generalized Additive Models
, 1984
"... Likelihood based regression models, such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariate effects. We introduce the Local Scotinq procedure which replaces the liner form C Xjpj by a sum of smooth functions C Sj(Xj) ..."
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Cited by 2413 (46 self)
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Likelihood based regression models, such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariate effects. We introduce the Local Scotinq procedure which replaces the liner form C Xjpj by a sum of smooth functions C Sj
A survey of generalpurpose computation on graphics hardware
, 2007
"... The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware acompelling platform for computationally demanding tasks in awide variety of application domains. In this report, we describe, summarize, and analyze the l ..."
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Cited by 545 (18 self)
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the latest research in mapping generalpurpose computation to graphics hardware. We begin with the technical motivations that underlie generalpurpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim
Projection Pursuit Regression
 Journal of the American Statistical Association
, 1981
"... A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, ..."
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Cited by 555 (6 self)
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A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures
Smooth Stabilization Implies Coprime Factorization
, 1989
"... This paper shows that coprime right factorizations exist for the input to state mapping of a continuous time nonlinear system provided that the smooth feedback stabilization problem be solvable for this system. In particular, it follows that feedback linearizable systems admit such factorizations. I ..."
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Cited by 459 (62 self)
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This paper shows that coprime right factorizations exist for the input to state mapping of a continuous time nonlinear system provided that the smooth feedback stabilization problem be solvable for this system. In particular, it follows that feedback linearizable systems admit such factorizations
Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models
 Journal of Business and Economic Statistics
, 2002
"... Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled wi ..."
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Cited by 684 (17 self)
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Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.
Learning with local and global consistency
 Advances in Neural Information Processing Systems 16
, 2004
"... We consider the general problem of learning from labeled and unlabeled data, which is often called semisupervised learning or transductive inference. A principled approach to semisupervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic stru ..."
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Cited by 666 (21 self)
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We consider the general problem of learning from labeled and unlabeled data, which is often called semisupervised learning or transductive inference. A principled approach to semisupervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic
Results 1  10
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