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ON ERROR FORMULAS FOR APPROXIMATION BY SUMS OF UNIVARIATE FUNCTIONS
, 2005
"... The purpose of the paper is to develop a new method for obtaining explicit formulas for the error of approximation of bivariate functions by sums of univariate functions. It should be remarked that formulas of this type have been known only for functions defined on a rectangle with sides parallel to ..."
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The purpose of the paper is to develop a new method for obtaining explicit formulas for the error of approximation of bivariate functions by sums of univariate functions. It should be remarked that formulas of this type have been known only for functions defined on a rectangle with sides parallel
Identification of an univariate function in a nonlinear dynamical model
"... Abstract — This paper addresses the problem of estimating, from measurement data corrupted by highly correlated noise, the shape of an unknown scalar and univariate function hidden in a known phenomenological model of the system. The method makes use of the Vapnik’s support vector regression to fi ..."
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Abstract — This paper addresses the problem of estimating, from measurement data corrupted by highly correlated noise, the shape of an unknown scalar and univariate function hidden in a known phenomenological model of the system. The method makes use of the Vapnik’s support vector regression
Finite state Markovchain approximations to univariate and vector autoregressions
 Economics Letters
, 1986
"... The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1. ..."
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Cited by 472 (0 self)
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The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1.
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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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.
The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis
 Cognit Psychol
, 2000
"... This individual differences study examined the separability of three often postulated executive functions—mental set shifting (‘‘Shifting’’), information updating and monitoring (‘‘Updating’’), and inhibition of prepotent responses (‘‘Inhibition’’)—and their roles in complex ‘‘frontal lobe’ ’ or ‘ ..."
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Cited by 626 (9 self)
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This individual differences study examined the separability of three often postulated executive functions—mental set shifting (‘‘Shifting’’), information updating and monitoring (‘‘Updating’’), and inhibition of prepotent responses (‘‘Inhibition’’)—and their roles in complex ‘‘frontal lobe
A unified theory of underreaction, momentum trading and overreaction in asset markets
, 1999
"... We model a market populated by two groups of boundedly rational agents: “newswatchers” and “momentum traders.” Each newswatcher observes some private information, but fails to extract other newswatchers’ information from prices. If information diffuses gradually across the population, prices underre ..."
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Cited by 577 (31 self)
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underreact in the short run. The underreaction means that the momentum traders can profit by trendchasing. However, if they can only implement simple (i.e., univariate) strategies, their attempts at arbitrage must inevitably lead to overreaction at long horizons. In addition to providing a unified account
We characterize the Kfunctionals for certain pairs of univariate function
, 1990
"... The Kfunctional was introduced by J. Peetre as a means of generating interpolation spaces. If X0, X, is a pair of quasinormed spaces which are continuously embedded in a Hausdorff space X, then their Kfunctional, defined for all f ~ X0 + X, , is ..."
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The Kfunctional was introduced by J. Peetre as a means of generating interpolation spaces. If X0, X, is a pair of quasinormed spaces which are continuously embedded in a Hausdorff space X, then their Kfunctional, defined for all f ~ X0 + X, , is
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 582 (23 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
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
Results 1  10
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141,807