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An Extension of Stolarsky Means to the multivariable case
, 2009
"... We give an extension of wellknown Stolarsky means to the multivariable case in a simple and applicable way. Some basic inequalities concerning this matter are also established with applications in Analysis and Probability Theory. ..."
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Cited by 3 (2 self)
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We give an extension of wellknown Stolarsky means to the multivariable case in a simple and applicable way. Some basic inequalities concerning this matter are also established with applications in Analysis and Probability Theory.
With Some Extensions to the Multivariate Case
"... The Economics & Statistics Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. The views expressed in the papers are solely the responsibility of the authors. A Characterization of the DickeyFuller Distribution, ..."
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The Economics & Statistics Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. The views expressed in the papers are solely the responsibility of the authors. A Characterization of the DickeyFuller Distribution,
Univariate and multivariate cases.
, 2012
"... Automatic variogram modeling by iterative least squares. ..."
Linear Recurrences With Constant Coefficients: The Multivariate Case
, 2000
"... While in the univariate case solutions of linear recurrences with constant coefficients have rational generating functions, we show that the multivariate case is much richer: even though initial conditions have rational generating functions, the corresponding solutions can have generating functions ..."
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Cited by 58 (16 self)
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While in the univariate case solutions of linear recurrences with constant coefficients have rational generating functions, we show that the multivariate case is much richer: even though initial conditions have rational generating functions, the corresponding solutions can have generating functions
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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. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m
A yieldfactor model of interest rates
 Math. Finance
, 1996
"... This paper presents a consistent and arbitragefree multifactor model of the term structure of interest rates in which yields at selected fixed maturities follow a parametric multivariate Markov diffusion process with “stochastic volatility. ” The yield of any zerocoupon bond is taken to be a matur ..."
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Cited by 665 (23 self)
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This paper presents a consistent and arbitragefree multifactor model of the term structure of interest rates in which yields at selected fixed maturities follow a parametric multivariate Markov diffusion process with “stochastic volatility. ” The yield of any zerocoupon bond is taken to be a
Empirical Processes Based on PseudoObservations II: the Multivariate Case
, 1998
"... this paper is to continue the study of empirical processes constructed from general pseudoobservations in the multivariate case. Examples of pseudoobservations will be given together with applications to copulas, multivariate regression and time series. 1 ..."
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Cited by 11 (2 self)
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this paper is to continue the study of empirical processes constructed from general pseudoobservations in the multivariate case. Examples of pseudoobservations will be given together with applications to copulas, multivariate regression and time series. 1
On the information loss in memoryless systems: The multivariate case
 in Proc. Int. Zurich Seminar on Communications (IZS
, 2012
"... ar ..."
A support vector method for multivariate performance measures
 Proceedings of the 22nd International Conference on Machine Learning
, 2005
"... This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F1score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially nonlinear per ..."
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Cited by 305 (6 self)
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This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F1score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1norm penalty term. The problem as formulated is convex but the memor ..."
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Cited by 334 (2 self)
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but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can
Results 1  10
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219,059