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Directional LogDensity Estimation with Application to Astronomy
, 1999
"... This paper develops logdensity estimation for directional data. The methodology is to use expansions with respect to spherical harmonics followed by estimating the unknown parameters by maximum likelihood. Automatic algorithms for model selection in terms of basis inclusion/exclusion are adapted fo ..."
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This paper develops logdensity estimation for directional data. The methodology is to use expansions with respect to spherical harmonics followed by estimating the unknown parameters by maximum likelihood. Automatic algorithms for model selection in terms of basis inclusion/exclusion are adapted
A new approach to the maximum flow problem
 JOURNAL OF THE ACM
, 1988
"... All previously known efficient maximumflow algorithms work by finding augmenting paths, either one path at a time (as in the original Ford and Fulkerson algorithm) or all shortestlength augmenting paths at once (using the layered network approach of Dinic). An alternative method based on the pre ..."
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Cited by 672 (33 self)
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of the algorithm running in O(nm log(n²/m)) time on an nvertex, medge graph. This is as fast as any known method for any graph density and faster on graphs of moderate density. The algorithm also admits efticient distributed and parallel implementations. A parallel implementation running in O(n²log n) time using
Clustering via Mode Seeking by Direct Estimation of the Gradient of a LogDensity
"... Abstract. Mean shift clustering nds the modes of the data probability density by identifying the zero points of the density gradient. Since it does not require to x the number of clusters in advance, the mean shift has been a popular clustering algorithm in various application elds. A typical implem ..."
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Cited by 3 (3 self)
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propose a method to directly estimate the gradient of the logdensity without going through density estimation. The proposed method gives the global solution analytically and thus is computationally efficient. We then develop a meanshiftlike xedpoint algorithm to nd the modes of the density
An effective method for high dimensional logdensity ANOVA estimation, with application to nonparametric graphical model building
 Statist. Sinica
, 2006
"... The logdensity functional ANOVA model provides a powerful framework for the estimation and interpretation of high dimensional densities. Existing methods for fitting such a model require repeated numerical integration of high dimensional functions, and are infeasible in problems of dimension large ..."
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Cited by 3 (0 self)
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The logdensity functional ANOVA model provides a powerful framework for the estimation and interpretation of high dimensional densities. Existing methods for fitting such a model require repeated numerical integration of high dimensional functions, and are infeasible in problems of dimension
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
MDL Procedures with ℓ1 Penalty and their Statistical Risk
"... Abstract — We review recently developed theory for the Minimum Description Length principle, penalized likelihood and its statistical risk. An information theoretic condition on a penalty pen(f) yields the conclusion that the optimizer of the penalized log likelihood criterion log 1/likelihood(f) + ..."
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results are presented for the regression case. Other examples involve logdensity estimation and Gaussian graphical statistical models. I.
Detecting High LogDensities – an O(n 1/4) Approximation for Densest kSubgraph
"... In the Densest kSubgraph problem, given a graph G and a parameter k, one needs to find a subgraph of G induced on k vertices that contains the largest number of edges. There is a significant gap between the best known upper and lower bounds for this problem. It is NPhard, and does not have a PTAS ..."
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Cited by 23 (1 self)
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unless NP has subexponential time algorithms. On the other hand, the current best known algorithm of Feige, Kortsarz and Peleg [FKP01], gives an approximation ratio of n1/3−ε for some specific ε> 0 (estimated by those authors at around ε = 1/60). We present an algorithm that for every ε> 0
A note on maximum likelihood density estimation using a proxy of the KullbackLeibler distance.
"... Given a random sample from a continuous and positive density f , the logistic transformation is applied and a log density estimate is provided by using Bsplines. The log density estimate maximizes the likelihood function which has equivalent solution when subject to a constraint that guarantees ..."
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Given a random sample from a continuous and positive density f , the logistic transformation is applied and a log density estimate is provided by using Bsplines. The log density estimate maximizes the likelihood function which has equivalent solution when subject to a constraint that guarantees
Triogram models
 Journal of the American Statistical Association
, 1998
"... In this paper we introduce the Triogram method for function estimation using piecewise linear, bivariate splines based on an adaptively constructed triangulation. We illustrate the technique for bivariate regression and logdensity estimation and indicate how our approach can be applied directly to ..."
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Cited by 21 (4 self)
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In this paper we introduce the Triogram method for function estimation using piecewise linear, bivariate splines based on an adaptively constructed triangulation. We illustrate the technique for bivariate regression and logdensity estimation and indicate how our approach can be applied directly
Results 1  10
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