Copula density estimation by total variation penalized likelihood with linear equality constraints

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...

Copula 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...

Density estimation by total variation penalized likelihood driven by the sparsity `1 information criterion

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...

Density estimation by total variation penalized likelihood driven by the sparsity ` 1 information criterion

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...

Penalized Likelihood Density Estimation :