Add to ORKGAbstract. A proposal of van der Vaart (1996) for an adaptive estimator of a location parameter from a family of normal scale mixtures is explored. Recent developments in convex optimization have dramatically improved the computational feasibility of the Kiefer and Wolfowitz (1956) nonparametric maximum likelihood estimator for general mixture models and yield an effective strategy for estimating the efficient score function for the location parameter in this setting. The approach is extended to regression and performance is evaluated with a small simulation experiment
Abstract — Variable selection is a crucial part of building regression models, and is preferably don...
This article proposes a robust way to estimate the scale parameter of a generalised centered Gaussia...
Abstract. Estimation of mixture densities for the classical Gaussian com-pound decision problem and ...
Abstract. A proposal of Van der Vaart (1996) for an adaptive estimator of a location parameter from ...
We model a regression density flexibly so that at each value of the covariates the density is a mixt...
We study location-scale mixture priors for nonparametric statistical problems, including multivariat...
We study location-scale mixture priors for nonparametric statistical problems, including multivariat...
I will start by presenting some Hellinger accuracy results for the Nonparametric Maximum Likelihood ...
In these notes, we present and review dierent methods based on maximum-likelihood estimation for lea...
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian grap...
In order to overcome the problems due to the unboundedness of the likelihood, constrained approaches...
The Expectation-Maximization (EM) algorithm has been predominantly used to approximate the maximum l...
This thesis offers a probabilistic solution to robust estimation using a novel adaptive estimator. R...
This thesis offers a probabilistic solution to robust estimation using a novel adaptive estimator. R...
This thesis offers a probabilistic solution to robust estimation using a novel adaptive estimator. R...
Abstract — Variable selection is a crucial part of building regression models, and is preferably don...
This article proposes a robust way to estimate the scale parameter of a generalised centered Gaussia...
Abstract. Estimation of mixture densities for the classical Gaussian com-pound decision problem and ...
Abstract. A proposal of Van der Vaart (1996) for an adaptive estimator of a location parameter from ...
We model a regression density flexibly so that at each value of the covariates the density is a mixt...
We study location-scale mixture priors for nonparametric statistical problems, including multivariat...
We study location-scale mixture priors for nonparametric statistical problems, including multivariat...
I will start by presenting some Hellinger accuracy results for the Nonparametric Maximum Likelihood ...
In these notes, we present and review dierent methods based on maximum-likelihood estimation for lea...
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian grap...
In order to overcome the problems due to the unboundedness of the likelihood, constrained approaches...
The Expectation-Maximization (EM) algorithm has been predominantly used to approximate the maximum l...
This thesis offers a probabilistic solution to robust estimation using a novel adaptive estimator. R...
This thesis offers a probabilistic solution to robust estimation using a novel adaptive estimator. R...
This thesis offers a probabilistic solution to robust estimation using a novel adaptive estimator. R...
Abstract — Variable selection is a crucial part of building regression models, and is preferably don...
This article proposes a robust way to estimate the scale parameter of a generalised centered Gaussia...
Abstract. Estimation of mixture densities for the classical Gaussian com-pound decision problem and ...