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We proof that statistically, the maximum likelihood location estimator of exponential power distribution is strict super robust, when p < 1.
For normal canonical models, and more generally a vast arrayof general spherically symmetric location-scale models with a residual vector, we consider estimatingthe (univariate) location parameter whe...
We propose an efficient scheme for generating fake network traffic to disguise the real event notification in the presence of a global eavesdropper, which is especially relevant for the quality of ser...
The paper concerns the estimation of univariabb (multivariate) linear regression parameters based on the independent normally distributed random variables (vectors) with unknown variance (p. d. cov...
Some statistical completeness results are proved for location families. For the proofs in the first step some generalizations of the Wiener closure theorem to LP-spaces with weight functions are es...
Based on the methods provided in Caeiro and Gomes (2002) and Fraga Alves (2001), a new class of location invariant Hill-type estimators is derived in this paper. Its asymptotic distributional represen...
The spatial structure of a class of superprocesses which arise as limits in distribution of a class of interacting particle systems with location dependent branching is investigated. The criterion of ...
Stein-type estimation of location vectors is discussed with the aid of the theory of electrostatics. We consider a class of estimating functions and assess the superiority of an estimating equation by...
This paper addresses the issue of deriving estimators improving on the best location equivariant (or Pitman) estimator under the squared error loss when a location parameter is restricted to a bounded...
We make a conjugate analysis for the five location-dispersion families including the normal, the transformed gamma and the von Mises distributions. The five families are introduced through the require...
For estimating the median θ of a spherically symmetric univariate distribution under squared error loss, when θ is known to be restricted to an interval [−m,m], m known, we derive sufficient con...
Point and region estimation may both be described as specific decision problems. In point estimation,the action space is the set of possible values of the quantity on interest; in region estimation, t...
Singular Value Decomposition (SVD) is the basic body of many statistical algorithms and few users question whether SVD is properly handling its job. SVD aims at evaluating the decomposition that bes...

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