Multivariate Density Estimation
Multivariate Density Estimation and Visualization
... other density with the same scale will have more complicated structure and will require a smaller bandwidth to more accurately estimate those ...(1989). Multivariate extensions are discussed by Sain et ...
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Semiparametric Multivariate Density Estimation for Positive Data Using Copulas.
... a multivariate semiparametric density estimation method which is robust to both the boundary and the curse of dimension ...underlying multivariate density, and semiparametric ...
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Semiparametric multivariate density estimation for positive data using copulas
... a multivariate semiparametric density estimation method which is robust to both the boundary and the curse of dimension ...underlying multivariate density, and semiparametric ...
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Semiparametric Multivariate Density Estimation Using Copulas and Shape-Constraints
... for multivariate LCMM by focusing on the per- fomance of classification ...studied multivariate LCMM via the copula ...joint density function of LCMM is given ...
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Pricing and Hedging of Mortgage-Backed Securities: A Multivariate Density Estimation Approach
... Figure 2 shows that there are several reasons for choosing the TBA market during the post 1986 time period to investigate MBS pricing using the MDE methodology. First, during 1985 and 1986, interest rates dramatically ...
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Multivariate mixtures of Erlangs for density estimation under censoring
... valuable multivariate density estimation technique to analyze realistic data, even in incomplete data settings, and to model the dependence directly in a low dimensional ...
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Multivariate mixtures of Erlangs for density estimation under censoring
... infinite multivariate mixture of Erlang ...valuable multivariate density estimation technique to analyze realistic data, even in incomplete data settings, and to model the dependence directly ...
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The L1-consistency of Dirichlet mixtures in multivariate Bayesian density estimation
... univariate density estimation are obtained by balancing the size of some sieves in the parameter space and the prior probability of the component of the ...in multivariate density ...
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Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation
... kernel density estimation has been much slower in the multivariate than univariate ...of multivariate density estimation attention has focused on diagonal bandwidth ...
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Nonparametric density estimation for multivariate bounded data using two nonnegative multiplicative bias correction methods
... Rafael Kawka 2,3 Technical University of Dortmund, Department of Statistics, Vogelpothsweg 87, 44227 Dortmund, Germany Abstract In this article we propose two new Multiplicative Bias Correction (MBC) techniques for ...
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Regularized Non-Parametric Multivariate Density and Conditional Density Estimation
... Non-Parametric Multivariate Density and Conditional Density Estimation Peter Krauthausen and Uwe ...both multivariate non-parametric density and conditional density ...
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Nonparametric Density Estimation for Multivariate Bounded Data
... This paper proposes a nonparametric estimator for density functions of multivariate bounded data. The estimator is based on a gamma kernel or a local linear kernel when the support of the variable is ...
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Nonparametric density estimation for multivariate bounded data.
... for density functions of multivariate bounded ...underlying density so that the estimators are robust to the boundary ...the multivariate time series case, the censored data case or further ...
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Maximum likelihood estimation of a multivariate log-concave density
... kernel density estimator introduced in the first ...kernel density estimator, we included several possible bandwidth selectors in our ...kernel density estimators in most situations, and considerably ...
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Approximate inference of the bandwidth in multivariate kernel density estimation
... true density (Loader, 1999; Sheather and Jones, 1991; Terrell, 1990; Duong and Hazelton, 2005; Chiu, 1991; Hall, 1982; Jones and Henderson, ...with multivariate data in the same way it does with univariate ...
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Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC
... Kernel density estimation for multivariate data is an important technique that has a wide range of applications in econometrics and ...in multivariate kernel density estimation ...
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Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC
... resulting density estimates using known bivariate ...known multivariate densities and finds that our MCMC approach to bandwidth selection works much better than the normal reference ...
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Tree-based multivariate regression and density estimation with right-censored data
... Section 2.2 alluded to the fact that a given parameter of interest, c 0 ; can arise as the risk minimizer for a number of different loss functions, say L 1 ; y ; L m (e.g., different los[r] ...
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Bayesian Adaptive Bandwidth Kernel Density Estimation of Irregular Multivariate Distributions
... tail-adaptive density estimator used for each generated sample, we applied the random-walk Metropolis-Hastings algorithm to the update of all bandwidths in the univariate situation (or all components of the ...
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Tree-based Multivariate Regression and Density Estimation with Right-Censored Data
... Within each sample size, the four test sample risk estimates were averaged over the B = 100 repetitions. Method 1 and Method 2 were compared by forming the ratio of Method 2’s average risk to that of Method 1, separately ...
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