Mechanized experiment planning in automaton-environment systems
Méthodes de segmentation non paramétriques
Minimax and Bayes estimation when the loss function is unknown
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 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 prediction
Let U₀ be a random vector taking its values in a measurable space and having an unknown distribution P and let U₁,...,Uₙ and be independent, simple random samples from P of size n and m, respectively. Further, let be real-valued functions defined on the same space. Assuming that only the first sample is observed, we find a minimax predictor d⁰(n,U₁,...,Uₙ) of the vector with respect to a quadratic errors loss function.
Minimax Prediction for the Multinomial and Multivariate Hypergeometric Distributions
A problem of minimax prediction for the multinomial and multivariate hypergeometric distribution is considered. A class of minimax predictors is determined for estimating linear combinations of the unknown parameter and the random variable having the multinomial or the multivariate hypergeometric distribution.
Minimax prediction of a sample distribution function
Minimax prediction of the difference of multinomial random variables
Minimax prediction of the difference of sample distribution functions
Minimax prediction of the sum of processes
Minimax prediction under random sample size
A class of minimax predictors of random variables with multinomial or multivariate hypergeometric distribution is determined in the case when the sample size is assumed to be a random variable with an unknown distribution. It is also proved that the usual predictors, which are minimax when the sample size is fixed, are not minimax, but they remain admissible when the sample size is an ancillary statistic with unknown distribution.