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