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In estimation of the normal covariance matrix, finding a least favorable sequence of prior distributions has been an open question for a long time. This paper addresses the classical problem and accomplishes the specification of such a sequence, which establishes minimaxity of the best equivariant estimator. This result is extended to the estimation of scale parameter matrix in an elliptically contoured distribution model. The methodology based on a least favorable sequence of prior distributions is applied to both restricted and non-restricted cases of parameters, and we give some examples which show minimaxity of the best equivariant estimators under restrictions of scale parameter matrix. [ABSTRACT FROM AUTHOR]

For translation and scale equivariant estimators of location, inequalities connecting tail behavior and the finite-sample breakdown point are proved, analogous to those established by He et al. (1990, Econometrika, 58, 1195–1214) for monotone and translation equivariant estimators. Some other inequalities are given as well, enabling to establish refined bounds and in some cases exact values for the tail behavior under heavy- and light-tailed distributions. The inequalities cover translation and scale equivariant estimators in great generality, and they involve new breakdown-related quantities, whose relations to the breakdown point are discussed. The worth of tail-behavior considerations in robustness theory is demonstrated on examples, showing the impact of the basic two techniques in robust estimation: trimming and averaging. The mathematical language employs notions from regular variation theory. [ABSTRACT FROM AUTHOR]