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1,165
Nonparametric Estimation of StatePrice Densities Implicit In Financial Asset Prices
 JOURNAL OF FINANCE
, 1997
"... Implicit in the prices of traded financial assets are ArrowDebreu prices or, with continuous states, the stateprice density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and derive its asymptotic sampling theory. This estimator provides an arbitragefree metho ..."
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Cited by 339 (6 self)
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Implicit in the prices of traded financial assets are ArrowDebreu prices or, with continuous states, the stateprice density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and derive its asymptotic sampling theory. This estimator provides an arbitrage
The Variance Gamma Process and Option Pricing.
 European Finance Review
, 1998
"... : A three parameter stochastic process, termed the variance gamma process, that generalizes Brownian motion is developed as a model for the dynamics of log stock prices. The process is obtained by evaluating Brownian motion with drift at a random time given by a gamma process. The two additional par ..."
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Cited by 365 (34 self)
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densities are estimated for data on the S&P500 Index and the prices of options on this Index. It is observed that the statistical density is symmetric with some kurtosis, while the risk neutral density is negatively skewed with a larger kurtosis. The additional parameters also correct for pricing biases
Wavelet shrinkage: asymptopia
 Journal of the Royal Statistical Society, Ser. B
, 1995
"... Considerable e ort has been directed recently to develop asymptotically minimax methods in problems of recovering in nitedimensional objects (curves, densities, spectral densities, images) from noisy data. A rich and complex body of work has evolved, with nearly or exactly minimax estimators bein ..."
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Cited by 295 (36 self)
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Considerable e ort has been directed recently to develop asymptotically minimax methods in problems of recovering in nitedimensional objects (curves, densities, spectral densities, images) from noisy data. A rich and complex body of work has evolved, with nearly or exactly minimax estimators
Keywords Multidimensional · Data density estimation · Discrete cosine transform
"... Multidimensional data density estimation in P2P ..."
Estimating a Dirichlet distribution
, 2000
"... The Dirichlet distribution and its compound variant, the Dirichletmultinomial, are two of the most basic models for proportional data, such as the mix of vocabulary words in a text document. Yet the maximumlikelihood estimate of these distributions is not available in closedform. This paper descr ..."
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Cited by 207 (2 self)
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The Dirichlet distribution and its compound variant, the Dirichletmultinomial, are two of the most basic models for proportional data, such as the mix of vocabulary words in a text document. Yet the maximumlikelihood estimate of these distributions is not available in closedform. This paper
Charting a Manifold
 Advances in Neural Information Processing Systems 15
, 2003
"... this paper we use m i ( j ) N ( j ; i , s ), with the scale parameter s specifying the expected size of a neighborhood on the manifold in sample space. A reasonable choice is s = r/2, so that 2erf(2) > 99.5% of the density of m i ( j ) is contained in the area around y i where the manifold i ..."
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Cited by 206 (7 self)
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this paper we use m i ( j ) N ( j ; i , s ), with the scale parameter s specifying the expected size of a neighborhood on the manifold in sample space. A reasonable choice is s = r/2, so that 2erf(2) > 99.5% of the density of m i ( j ) is contained in the area around y i where the manifold
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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of this section gives a practical description of the Cartesian case. The following parts are dedicated to general theory, SNR and error considerations, and sensitivity assessment. Sensitivity Encoding With Cartesian Sampling of kSpace In twodimensional (2D) Fourier imaging with common Cartesian sampling of k
OneDimensional and MultiDimensional Substring Selectivity Estimation
, 2000
"... this paper,we uw pru,C cou,CF1p fix trees (PSTs) as the basic datastruC tur forsu,3kRk, selectivity estimation. For the 1D problem, we present a novel techniqu called MO (Maximal Overlap). We then develop and analyze two 1D estimation algorithms, MOC and MOLC,based on MO and a constraintbased cha ..."
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Cited by 36 (7 self)
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this paper,we uw pru,C cou,CF1p fix trees (PSTs) as the basic datastruC tur forsu,3kRk, selectivity estimation. For the 1D problem, we present a novel techniqu called MO (Maximal Overlap). We then develop and analyze two 1D estimation algorithms, MOC and MOLC,based on MO and a constraint
Approximating MultiDimensional Aggregate Range Queries Over Real Attributes
, 2000
"... Finding approximate answers to multidimensional range queries over real valued attributes has significant applications in data exploration and database query optimization. In this paper we consider the following problem: given a table of d attributes whose domain is the real numbers, and a quer ..."
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Cited by 85 (9 self)
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to generalize kernel density estimators, and how to apply them on the multidimensional query approxim...
Support Vector Density Estimation
, 1999
"... > (x); (1.1) 1. `(x) = ( 1; x ? 0 0; otherwise Generic author design sample pages 1999/07/12 15:50 2 Support Vector Density Estimation where instead of knowing the distribution function F (x) we are given the iid (independently and identically distributed) data x 1 ; : : : ; x ` (1.2) genera ..."
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Cited by 45 (2 self)
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> (x); (1.1) 1. `(x) = ( 1; x ? 0 0; otherwise Generic author design sample pages 1999/07/12 15:50 2 Support Vector Density Estimation where instead of knowing the distribution function F (x) we are given the iid (independently and identically distributed) data x 1 ; : : : ; x ` (1.2
Results 1  10
of
1,165