Werner Fieger

MINIMAX- AND Γ-MINIMAX ESTIMATION OF A BOUNDED NORMAL MEAN UNDER LINEX LOSS

Estimating a bounded normal mean is considered under LINEX loss

On least favourable two point priors and minimax estimators under absolute error loss

L_\alpha (\theta, d) = s \cdot \biggl( \exp \bigl\{ \alpha (d - \theta) \bigr\} - \alpha (d - \theta) - 1 \biggr),

Minimax estimation under convex loss when the parameter interval is bounded

where

Minimax estimation for the bounded mean of a bivariate normal distribution

\alpha \ne 0

A characterization of the normal distribution by sufficiency of the least squares estimation

and

Äquivariante Schätzfunktionen und Normalverteilungsannahme im linearen Modell

s > 0

Transformationen, die die Normalverteilung charakterisieren

are fixed constants. It is shown that a two point prior is least favourable in case the parameter interval is small enough. So minimax and

Zur Existenz der höheren Momente von Punktprozessen

\Gamma