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77
Semiparametrically efficient rankbased inference for shape I: Optimal rankbased tests for sphericity
 Ann. Statist
, 2006
"... A class of Restimators based on the concepts of multivariate signed ranks and the optimal rankbased tests developed in Hallin and Paindaveine [Ann. Statist. 34 (2006)] is proposed for the estimation of the shape matrix of an elliptical distribution. These Restimators are rootn consistent under a ..."
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Cited by 48 (32 self)
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any radial density g, without any moment assumptions, and semiparametrically efficient at some prespecified density f. When based on normal scores, they are uniformly more efficient than the traditional normaltheory estimator based on empirical covariance matrices (the asymptotic normality of which
Density Estimation by the Penalized Combinatorial Method
"... Let f be an unknown multivariate density belonging to a prespecified parametric class of densities, Fk, where k is unknown, but Fk ae Fk+1 for all k and each Fk has finite VapnikChervonenkis dimension. Given an i.i.d. sample of size n drawn from f, we show that it is possible to select automatica ..."
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Cited by 7 (3 self)
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Let f be an unknown multivariate density belonging to a prespecified parametric class of densities, Fk, where k is unknown, but Fk ae Fk+1 for all k and each Fk has finite VapnikChervonenkis dimension. Given an i.i.d. sample of size n drawn from f, we show that it is possible to select
Locally Scaled Density Based Clustering
 In ICANNGA 2007, LNCS
, 2007
"... Abstract. Density based clustering methods allow the identification of arbitrary, not necessarily convex regions of data points that are densely populated. The number of clusters does not need to be specified beforehand; a cluster is defined to be a connected region that exceeds a given density thre ..."
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Cited by 7 (0 self)
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cluster is grown until the density falls below a prespecified ratio of the center point’s density. The resulting clustering technique is able to identify clusters of arbitrary shape on noisy backgrounds that contain significant density gradients. The focus of this paper is to automate the process
Empirical Evaluation of DataBased Density Estimation Procedures
, 2006
"... We discuss an approach to estimate density of a stochastic process observed by simulation so that the density estimate meets a prespecified precision. The characteristics of the steadystate distribution of the stochastic process are estimated via a empirical histogram. This paper discusses impleme ..."
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We discuss an approach to estimate density of a stochastic process observed by simulation so that the density estimate meets a prespecified precision. The characteristics of the steadystate distribution of the stochastic process are estimated via a empirical histogram. This paper discusses
EMPIRICAL EVALUATION OF DATABASED DENSITY ESTIMATION
"... This paper discusses implementation of a sequential procedure to estimate the steadystate density of a stochastic process. The procedure computes sample densities at certain points and uses Lagrange interpolation to estimate the density f(x). Even though the proposed sequential procedure is a heuri ..."
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heuristic, it does have strong basis. Our empirical results show that the procedure gives density estimates that satisfy a prespecified precision requirement. An experimental performance evaluation demonstrates the validity of using the procedure to estimate densities. 1
The Annals of Statistics SEMIPARAMETRICALLY EFFICIENT RANKBASED INFERENCE FOR SHAPE II. OPTIMAL RESTIMATION OF SHAPE
"... A class of Restimators, based on the concepts of multivariate signed ranks and the optimal rankbased tests developed in Hallin and Paindaveine (2006a), is proposed for the estimation of the shape matrix of an elliptical distribution. These Restimators are rootn consistent under any radial dens ..."
Abstract
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density g, without any moment assumptions, and semiparametrically efficient at some prespecified density f. When based on normal scores, they are uniformly more efficient than the traditional normaltheory estimator, based on empirical covariance matrices (the asymptotic normality of which moreover
Modeling NMR Lineshapes Using Logspline Density Functions
 J. Magn. Reson
, 1997
"... this article, we prove that the characteristic function representation is correct under very general conditions. RCF approximated the complexvalued distorting function in the time domain by two regression splines, one constrained to be an even function and the other to be an odd function, so that t ..."
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Cited by 3 (0 self)
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values of ø in fitting the model. As an alternative to the Hermitian spline model, we propose a flexible model in which the distorting function is proportional to a logspline density function in the frequency domain and thus constrained to be nonnegative definite in the time domain (section 3
DensityBased Centroid Approximation for Initializing Iterative Clustering Algorithms
, 2002
"... We present KDI (Kernel Density Initialization), a densitybased procedure for approximating centroids for the initialization step of iterationbased clustering algorithms. We show empirically that a rather low number of distance calculations in conjunction with a fast algorithm for finding the hi ..."
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Cited by 1 (0 self)
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We present KDI (Kernel Density Initialization), a densitybased procedure for approximating centroids for the initialization step of iterationbased clustering algorithms. We show empirically that a rather low number of distance calculations in conjunction with a fast algorithm for finding
Testing Hypotheses About the Number of Factors in Large Factor Models
 Econometrica
"... In this paper we study highdimensional time series that have the generalized dynamic factor structure. We develop a test of the null of k0 factors against the alternative that the number of factors is larger than k0 but no larger than k1> k0. Our test statistic equals maxk0<k≤k1 γk − γk+1 / γ ..."
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Cited by 48 (1 self)
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/ γk+1 − γk+2, where γi is the ith largest eigenvalue of the smoothed periodogram estimate of the spectral density matrix of data at a prespecified frequency. We describe the asymptotic distribution of the statistic, as the dimensionality and the number of observations rise, as a function
Adaptive density estimation using an orthogonal series for global illumination
"... In MonteCarlo photontracing methods energycarrying particles are traced in an environment to generate hit points on object surfaces for simulating global illumination. The surface illumination can be reconstructed from particle hit points by solving a density estimation problem using an orthogona ..."
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on orthogonal series density estimation use a prespecified or fixed number m of terms of an orthogonal series; this results in undesirable visual artifacts, i.e. either nearconstant shading across a surface which conceals the true illumination variation when m is very small or excessive illumination
Results 1  10
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77