For three classes of limit processes, M-estimator limiting distributions are symmetric when the drift, the stochastic process, and the constraint cone are all even; for linear-process limits this evenness condition is also necessary under weak assumptions.
Rectangular Hull Confidence Regions for Multivariate Parameters
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abstract
We introduce three notions of multivariate median bias, namely, rectilinear, Tukey, and orthant median bias. Each of these median biases is zero under a suitable notion of multivariate symmetry. We study the coverage probabilities of rectangular hull of $B$ independent multivariate estimators, with special attention to the number of estimators $B$ needed to ensure a miscoverage of at most $\alpha$. It is proved that for estimators with zero orthant median bias, we need $B\geq c\log_2(d/\alpha)$ for some constant $c > 0$. Finally, we show that there exists an asymptotically valid (non-trivial) confidence region for a multivariate parameter $\theta_0$ if and only if there exists a (non-trivial) estimator with an asymptotic orthant median bias of zero.
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On the Symmetry of Limiting Distributions of M-estimators
For three classes of limit processes, M-estimator limiting distributions are symmetric when the drift, the stochastic process, and the constraint cone are all even; for linear-process limits this evenness condition is also necessary under weak assumptions.