Zusammenfassung: | A decision problem is characterized by a loss function V and opinion H. The pair (V, H) is said to be strongly stable iff for every sequence Fn→ωH, Gn→ωH and Ln→ V, Wn→ V uniformly, $\lim_{\varepsilon \downarrow 0} \lim \sup_{n\rightarrow \infty} \lbrack \int L_n(\theta, D_n(\varepsilon)) dF_n(\theta) - \inf_D \int L_n(\theta, D) dF_n(\theta)\rbrack = 0$ for every sequence Dn(ε) satisfying $\int W_n(\theta, D_n(\varepsilon)) dG_n(\theta) \leqq \inf_D \int W_n(\theta, D) dG_n(\theta) + \varepsilon.$ We show that squared error loss is unstable with any opinion if the parameter space is the real line and that any bounded loss function V(θ, D) that is continuous in θ uniformly in D is stable with any opinion H. Finally we examine the estimation or prediction case V(θ, D) = h(θ - D), where h is continuous, nondecreasing in (0, ∞) and nonincreasing in (-∞, 0) and has bounded growth. While these conditions are not enough to assure strong stability, various conditions are given that are sufficient. We believe that stability offers the beginning of a Bayesian theory of robustness.
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