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Discrete sampling
, 2008
"... Continuous record Realized volatility a b s t r a c t This paper motivates and introduces a twostage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed in [Jacod ..."
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Continuous record Realized volatility a b s t r a c t This paper motivates and introduces a twostage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed
Discrete Choice Methods with Simulation
, 2002
"... This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logi ..."
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Cited by 1277 (20 self)
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This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered
Compressive sampling
, 2006
"... Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired res ..."
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Cited by 1427 (15 self)
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Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired
Nonparametric inference for discretely sampled
, 2009
"... Given a sample from a discretely observed Lévy process X = (Xt)t≥0 of the finite jump activity, we study the problem of nonparametric estimation of the Lévy density ρ corresponding to the process X. Our estimator of ρ is based on a suitable inversion of the LévyKhintchine formula and a plugin devi ..."
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Given a sample from a discretely observed Lévy process X = (Xt)t≥0 of the finite jump activity, we study the problem of nonparametric estimation of the Lévy density ρ corresponding to the process X. Our estimator of ρ is based on a suitable inversion of the LévyKhintchine formula and a plug
Interpolation of DiscretelySampled Density Fields
, 2000
"... Abstract. We present a new technique for the interpolation of discretelysampled nonnegative scalar fields across regions of missing data. Any set of basis functions can be used, though the method is fastest when they are close to orthogonal. We show how the technique may be efficiently applied when ..."
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Abstract. We present a new technique for the interpolation of discretelysampled nonnegative scalar fields across regions of missing data. Any set of basis functions can be used, though the method is fastest when they are close to orthogonal. We show how the technique may be efficiently applied
Discrete sampling........................................................................................................................................ 6
"... Pairwise approximation of solvent accessible surface area (SASA)...................................................... 4 ..."
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Pairwise approximation of solvent accessible surface area (SASA)...................................................... 4
On the Use of Windows for Harmonic Analysis With the Discrete Fourier Transform
 Proc. IEEE
, 1978
"... AhmwThis Pw!r mak = available a concise review of data win compromise consists of applying windows to the sampled daws pad the ^ affect On the Of in the data set, or equivalently, smoothing the spectral samples. '7 of aoise9 m the ptesence of sdroag bar The two operations to which we subject ..."
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Cited by 645 (0 self)
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AhmwThis Pw!r mak = available a concise review of data win compromise consists of applying windows to the sampled daws pad the ^ affect On the Of in the data set, or equivalently, smoothing the spectral samples. '7 of aoise9 m the ptesence of sdroag bar The two operations to which we
Algorithms for Quantum Computation: Discrete Logarithms and Factoring
, 1994
"... A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a increase in computation time of at most a polynomial factor. It is not clear whether this is still true when quantum mechanics is taken into consi ..."
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Cited by 1103 (7 self)
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into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
 STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is develop ..."
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Cited by 1032 (76 self)
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In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework
Discrete sampling of functionals of Itô processes.
, 2004
"... For a multidimensional Itô process (Xt)t≥0 driven by a Brownian motion, we are interested in approximating the law of ψ () (Xs)s∈[0,T] , T> 0 deterministic, for a given functional ψ using a discrete sample of the process X. For various functionals (related to the maximum, to the integral of the p ..."
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For a multidimensional Itô process (Xt)t≥0 driven by a Brownian motion, we are interested in approximating the law of ψ () (Xs)s∈[0,T] , T> 0 deterministic, for a given functional ψ using a discrete sample of the process X. For various functionals (related to the maximum, to the integral
Results 1  10
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730,820