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A Multivariate Density Estimation Approach
, 1998
"... This chapter presents a nonparametric technique for pricing and hedging mortgagebacked securities (MBS). The particular technique used here is called multivariate density estimation (MDE). We find that MBS prices can be well described as a function of two interest rate factors; the level and slope ..."
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This chapter presents a nonparametric technique for pricing and hedging mortgagebacked securities (MBS). The particular technique used here is called multivariate density estimation (MDE). We find that MBS prices can be well described as a function of two interest rate factors; the level and slope
Support Vector Method for Multivariate Density Estimation
 Advances in Neural Information Processing Systems
, 2000
"... A new method for multivariate density estimation is developed based on the Support Vector Method (SVM) solution of inverse illposed problems. The solution has the form of a mixture of densities. This method with Gaussian kernels compared favorably to both Parzen's method and the Gaussian Mixtu ..."
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Cited by 51 (0 self)
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A new method for multivariate density estimation is developed based on the Support Vector Method (SVM) solution of inverse illposed problems. The solution has the form of a mixture of densities. This method with Gaussian kernels compared favorably to both Parzen's method and the Gaussian
Nonparametric Multivariate Density Estimation: A Comparative Study
 IEEE Trans. Signal Processing
, 1994
"... This paper algorithmically and empirically studies two major types of nonparametric multivariate density estimation techniques, where no assumption is made about the data being drawn from any of known parametric families of distribution. The first type is the popular kernel method (and several of it ..."
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Cited by 59 (1 self)
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This paper algorithmically and empirically studies two major types of nonparametric multivariate density estimation techniques, where no assumption is made about the data being drawn from any of known parametric families of distribution. The first type is the popular kernel method (and several
MULTIVARIATE DENSITY ESTIMATION WITH OPTIMAL MARGINAL PARZEN DENSITY
"... Abstract. Multivariate density estimation is an important problem that is frequently encountered in statistical learning and signal processing. One of the most popular techniques is Panen windowing, also referred to as kernel density estimation. Gaussianization is a procedure that allows one to esti ..."
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Abstract. Multivariate density estimation is an important problem that is frequently encountered in statistical learning and signal processing. One of the most popular techniques is Panen windowing, also referred to as kernel density estimation. Gaussianization is a procedure that allows one
Visualization of Multivariate Density Estimates with Level Set Trees
"... We present a method for visualization of multivariate functions. The method is based on a tree structure, built from separated parts of level sets of a function, which we call level set tree. The method is applied for visualization of estimates of multivarate density functions. With dierent grap ..."
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Cited by 12 (2 self)
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We present a method for visualization of multivariate functions. The method is based on a tree structure, built from separated parts of level sets of a function, which we call level set tree. The method is applied for visualization of estimates of multivarate density functions. With dierent
Multivariate Density Estimation: A Comparative Study
"... This paper is a continuation of the authors' earlier work [1], where a version of the Trav'en's [2] Gaussian clustering neural network (being a recursive counterpart of the EM algorithm) has been investigated. A comparative simulation study of the Gaussian clustering algorithm studied ..."
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Cited by 3 (2 self)
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provides a way of effectively estimating arbitrary and highly structured continuous densities on R d , at least for small d,...
Multivariate Density Estimation: a Support Vector Machine Approach
 In NIPS 12
, 1999
"... A Support Vector Machine (SVM) algorithm for multivariate density estimation is developed based on regularization principles and bounds on the convergence of empirical distribution functions. The algorithm is compared to Gaussian Mixture Models (GMMs). Our algorithm outperforms GMMs for data drawn f ..."
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Cited by 5 (1 self)
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A Support Vector Machine (SVM) algorithm for multivariate density estimation is developed based on regularization principles and bounds on the convergence of empirical distribution functions. The algorithm is compared to Gaussian Mixture Models (GMMs). Our algorithm outperforms GMMs for data drawn
Printed in Great Britain Adaptive Bayesian multivariate density estimation with Dirichlet mixtures
, 2013
"... We show that rateadaptive multivariate density estimation can be performed using Bayesian methods based on Dirichlet mixtures of normal kernels with a prior distribution on the kernel’s covariance matrix parameter. We derive sufficient conditions on the prior specification that guarantee convergen ..."
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We show that rateadaptive multivariate density estimation can be performed using Bayesian methods based on Dirichlet mixtures of normal kernels with a prior distribution on the kernel’s covariance matrix parameter. We derive sufficient conditions on the prior specification that guar
The Pricing and Hedging of MortgageBacked Securities: A Multivariate Density Estimation Approach
"... This chapter presents a nonparametric technique for pricing and hedging mortgagebacked securities (MBS). The particular technique used here is called multivariate density estimation (MDE). We find that MBS prices can be well described as a function of two interest rate factors; the level and slope ..."
Abstract

Cited by 3 (0 self)
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This chapter presents a nonparametric technique for pricing and hedging mortgagebacked securities (MBS). The particular technique used here is called multivariate density estimation (MDE). We find that MBS prices can be well described as a function of two interest rate factors; the level and slope
The Pricing and Hedging of MortgageBacked Securities: A Multivariate Density Estimation Approach
"... This chapter presents a nonparametric technique for pricing and hedging mortgagebacked securities (MBS). The particular technique used here is called multivariate density estimation (MDE). We find that MBS prices can be well described as a function of two interest rate factors; the level and slo ..."
Abstract
 Add to MetaCart
This chapter presents a nonparametric technique for pricing and hedging mortgagebacked securities (MBS). The particular technique used here is called multivariate density estimation (MDE). We find that MBS prices can be well described as a function of two interest rate factors; the level
Results 1  10
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1,527,385