Métriques et analyse factorielle comparative de deux tableaux ou plus
Minima of Vector Quadratic Forms with Applications to Statistics
Minimal cyclic random motion in and hyper-Bessel functions
Minimal Sufficient for the Group Divisible Partially Balanced Incomplete Clock Design (GD-PBIBD) with Interaction under an Eisenhart Modell II.
Minimax and bayes estimation in deconvolution problem*
We consider a deconvolution problem of estimating a signal blurred with a random noise. The noise is assumed to be a stationary Gaussian process multiplied by a weight function function εh where h ∈ L2(R1) and ε is a small parameter. The underlying solution is assumed to be infinitely differentiable. For this model we find asymptotically minimax and Bayes estimators. In the case of solutions having finite number of derivatives similar results were obtained in [G.K. Golubev and R.Z. Khasminskii,...
Minimax and Bayes estimation when the loss function is unknown
Minimax control of a multivariate time-continuous linear stochastic system
Minimax detection of signal in weighted Gaussian white noise.
Minimax estimation and prediction for random variables with bounded sum
Minimax Estimation for the Bounded Mean of a Bivariate Normal Distribution.
Minimax estimation of a class of functions of the scale parameter in the gamma and other distributions in the case of truncated parameter space
Minimax estimation of a cumulative distribution function for a special loss function
Minimax estimation of the parameters of the multivariate hypergeometric and multinomial distributions
Minimax estimation of the parameters of the multivariate hypergeometric distribution
Minimax Estimation Under Convex Loss when the Parameter Interval is Bounded.
Minimax Estimators and ...-Minimax Estimators for a Bounded Normal Mean Under the Loss ip(0, d) =I0-dIp
Minimax Estimators for a Bounded Location Parameter.
Minimax mutual prediction
The problems of minimax mutual prediction are considered for binomial and multinomial random variables and for sums of limited random variables with unknown distribution. For the loss function being a linear combination of quadratic losses minimax mutual predictors are determined where the parameters of predictors are obtained by numerical solution of some equations.
Minimax mutual prediction of multinomial random variables
The problem of minimax mutual prediction is considered for multinomial random variables with the loss function being a linear combination of quadratic losses connected with prediction of particular variables. The basic parameter of the minimax mutual predictor is determined by numerical solution of some equation.
Minimax nonparametric hypothesis testing for ellipsoids and Besov bodies