Oracle inequalities and nonparametric function estimation.
Posterior regret Γ-minimax estimation in a normal model with asymmetric loss function
The problem of posterior regret Γ-minimax estimation under LINEX loss function is considered. A general form of posterior regret Γ-minimax estimators is presented and it is applied to a normal model with two classes of priors. A situation when the posterior regret Γ-minimax estimator, the most stable estimator and the conditional Γ-minimax estimator coincide is presented.
Procedures to Determine Optimum Two-Stage Sampling Plans by Attributes .
Randomized minimax estimators under simple random sampling from a finite population.
Robust Bayesian estimation in a normal model with asymmetric loss function
The problem of robust Bayesian estimation in a normal model with asymmetric loss function (LINEX) is considered. Some uncertainty about the prior is assumed by introducing two classes of priors. The most robust and conditional Γ-minimax estimators are constructed. The situations when those estimators coincide are presented.
Secretary Problems with Inspection Costs as a Game.
Semiparametric deconvolution with unknown noise variance
This paper deals with semiparametric convolution models, where the noise sequence has a gaussian centered distribution, with unknown variance. Non-parametric convolution models are concerned with the case of an entirely known distribution for the noise sequence, and they have been widely studied in the past decade. The main property of those models is the following one: the more regular the distribution of the noise is, the worst the rate of convergence for the estimation of the signal’s density...
Semiparametric deconvolution with unknown noise variance
This paper deals with semiparametric convolution models, where the noise sequence has a Gaussian centered distribution, with unknown variance. Non-parametric convolution models are concerned with the case of an entirely known distribution for the noise sequence, and they have been widely studied in the past decade. The main property of those models is the following one: the more regular the distribution of the noise is, the worst the rate of convergence for the estimation of the signal's density...
Some Applications of Anderson's Inequality to Some Optimality Criteria of the Generalized Least Squares Estimator.
Some investigations in minimax estimation theory [Book]
Some problems of prediction of the difference of random variables
Stabilité et instabilité du risque minimax pour des variables indépendantes équidistribuées
Stability and conditional Γ-minimaxity in Bayesian inference
Two concepts of optimality corresponding to Bayesian robust analysis are considered: conditional Γ-minimaxity and stability. Conditions for coincidence of optimal decisions of both kinds are stated.
Stein estimation for infinitely divisible laws
Unbiased risk estimation, à la Stein, is studied for infinitely divisible laws with finite second moment.
Systematic estimation and prediction for processes with bounded sum
The Necessity of Strongly Subadditive Capacities for Neyman-Pearson Minimax Tests.
Topologies associated to some decision statistical functions
Two-point priors and minimax estimation of a bounded parameter under convex loss
The problem of minimax estimation of a parameter θ when θ is restricted to a finite interval [θ₀,θ₀+m] is studied. The case of a convex loss function is considered. Sufficient conditions for existence of a minimax estimator which is a Bayes estimator with respect to a prior concentrated in two points θ₀ and θ₀+m are obtained. An example is presented.
Una clase de problemas de decisión bajo incertidumbre parcial.
Se estudia el Problema de Decisión cuando el ambiente es de incertidumbre parcial, en el sentido de que la distribución a priori -que se supone absolutamente continua- sobre el espacio de estados -un intervalo real- no se conoce en su totalidad, sino que tan sólo se posee información respecto a las probabilidades de algunos subintervalos de Θ o acotaciones de éstas, así como algunas restricciones sobre los momentos y ciertas generalizaciones de éstas, dentro de este contexto.Además de las correspondientes...
Ungünstige Verteilungen bei einparametrigen Parameterschätzproblemen.