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We proof that statistically, the maximum likelihood location estimator of exponential power distribution is strict super robust, when p < 1.
The paper is concerned with the maximum likelihood estimator (MLE) of the unknown drift parameterθ∈Rin the continuous-time regression model Xt =θt+Bt +BHt,t ∈[0, T] whereBt is the Brownian motion and ...
In this article, we discuss the composite likelihood estimation of sparse Gaussian graph-ical models. When there are symmetry constraints on the concentration matrix or partial correlation matrix, the...
The Ising model is a useful tool for studying complex interactions within a system. The estimation of such a model, however, is rather challenging, especially in the presence of high-dimensional param...
We consider probabilistic multinomial probit classification using Gaussian process (GP) priors. The challenges with the multiclass GP classification are the integration over the non-Gaussian posterior...
Cluster-weighted modeling (CWM) is a mixture approach for modeling the joint probability of a response variable and a set of explanatory variables. The parame-ters are estimated by means of the expect...
Gauss’ principle states that the maximum likelihood estimator of the parameter in a location family is the sample mean for all samples of all sample sizes if and only if the family is Gaussian. There ...
Variational methods for parameter estimation are an activere-search area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on recent work that ap...
Predictive recursion is an accurate and computationally efficient algorithm for nonparametric estimation of mixing densities in mixture models. In semiparametric mixture models, however, the algorithm...
Empirical likelihood is a powerful semi-parametric method increasingly investigated in the literature. However, most authors essentially focus on an i.i.d. setting. In the case of dependent data, the ...
Consider a random vector $(X,Y)$ and let $m(x)=E(Y|X=x)$. We are interested in testing $H_0 : m in {cal M}_{Theta,{cal G}} = {gamma(cdot,theta,g) : theta in Theta, g in {cal G}}$ for some known functi...
When in a full exponential family the maximum likelihood estimate (MLE) does not exist, the MLE may exist in the Barndorff-Nielsen completion of the family. We propose a practical algorithm for findin...

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