Copula density estimation by total variation penalized likelihood with linear equality constraints
نویسندگان
چکیده
منابع مشابه
Copula Density Estimation by Total Variation Penalized Likelihood with Linear Equality Constraints
A copula density is the joint probability density function (PDF) of a random vector with uniform marginals. An approach to bivariate copula density estimation is introduced that is based on a maximum penalized likelihood estimation (MPLE) with a total variation (TV) penalty term. The marginal unity and symmetry constraints for copula density are enforced by linear equality constraints. The TV-M...
متن کاملCopula Density Estimation by Total Variation Penalized Likelihood
A copula density is the joint probability density function (PDF) of a random vector with uniform marginals. An approach to bivariate copula density estimation is introduced that is based on a maximum penalized likelihood estimation (MPLE) with a total variation (TV) penalty term. The marginal unity and symmetry constraints for copula density are enforced by linear equality constraints. The TV-M...
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We propose a non-linear density estimator, which is locally adaptive, like wavelet estimators, and positive everywhere, without a logor root-transform. This estimator is based on maximizing a non-parametric log-likelihood function regularized by a total variation penalty. The smoothness is driven by a single penalty parameter, and to avoid cross-validation, we derive an information criterion ba...
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We propose a density estimator based on penalized likelihood and total variation. Driven by a single smoothing parameter, the nonlinear estimator has the properties of being locally adaptive and positive everywhere without a logor root-transform. For the fast selection of the smoothing parameter we employ the sparsity `1 information criterion. Furthermore the estimated density has the advantage...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2012
ISSN: 0167-9473
DOI: 10.1016/j.csda.2011.07.016