Multivariate mixtures of Erlangs for density estimation under censoring

Logspline Density Estimation under Censoring and Truncation

In this paper we consider logspline density estimation for data that may be lefttruncated or right-censored. For randomly left-truncated and right-censored data the product-limit estimator is known to be a consistent estimator of the survivor function, having a faster rate of convergence than many density estimators. The product-limit estimator and B-splines are used to construct the logspline ...

Adaptive wavelet estimation of a density from mixtures under multiplicative censoring

Comparison of presmoothing methods in kernel density estimation under censoring

1 Departamento de Matemáticas, Universidade da Coruña, Facultad de Ciencias, 15071 A Coruña (Spain) [email protected] 2 Department of Mathematics and University Center for Statistics, Katholieke Universiteit Leuven, Celestijnenlaan 200B, B-3001 Leuven (Heverlee), Belgium; Box 2400 [email protected] 3 Departamento de Matemáticas, Universidade da Coruña, Facultad de Informática, 15071 A...

L1-Consistency of Dirichlet Mixtures in Multivariate Bayesian Density Estimation

Density estimation, especially multivariate density estimation, is a fundamental problem in nonparametric inference. Dirichlet mixture priors are often used in practice for such problem. However, asymptotic properties of such priors have only been studied in the univariate case. We extend L1-consistency of Dirichlet mixutures in the multivariate density estimation setting. We obtain such a resu...

Adaptive Bayesian multivariate density estimation with Dirichlet mixtures

We show that rate-adaptive 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 convergence to a true density at a rate that is minimax optimal for the smoothness class to which the true ...