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 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 the difference of multinomial random variables
Minimax prediction of the difference of sample distribution functions
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.
Minimax state estimation for linear stochastic systems with an uncertain parameter
Minimum distance estimator for a hyperbolic stochastic partial differentialequation
We study a minimum distance estimator in -norm for a class ofnonlinear hyperbolic stochastic partial differential equations, driven by atwo-parameter white noise. The consistency and asymptotic normality of thisestimator are established under some regularity conditions on thecoefficients. Our results are applied to the two-parameterOrnstein-Uhlenbeck process.
Minimum entropy of error estimate for multi-dimensional parameter and finite-state-space observations
Minimum Kolmogorov Distance Estimates of Parameters an Parametrized Distributions.
Minimum Variance Unbiased Estimation for the Zero Class. Truncated Bivariate Poisson and Logarithmic Series Distributions.
Minimum Variance Unbiased Estimation in the Pareto Distribution.
Minimum Variance Unbiased Estimation of Left Truncated Multivariate Modified Power Series Distribution.
Minimum Variance Unbiased Estimation of Left Truncated Multivariate Power Series Distributions.
Minimum Variance Unbiased Estimation of Multivariate Modified Power Series Distribution.
Minimum Variance Unbiased Estimation of the Parameters of the Pareto Distribution.