In Theorem 3.1, we take up the over-and undershooting probabilities used as risk in Huber (1968) to determine a finite sample minimax estimator of location. By means of a s-o expansion, we refine the corresponding f-o translation by Rieder (1980) , providing a closer link to finite sample optimality.
The closed form expressions in (1.1), in particular under certain symmetry assumptions, allows us to tackle corresponding (uniform) higher order optimality problems, so that we may check whether Pfanzagl (1979) 's catchword "First order efficiency implies second order efficiency" survives when passing to neighborhoods around the ideal model, which-at least under symmetry-indeed (partially) holds.
In this setting, we see that Huber-type location M-estimators remain optimal in second order sense, and we even may determine the s-o-o clipping height c 1 = c 1 (r, n) which in fact is slightly lower (O(n −1/2 )) than the f-o-o one. So in fact we only retain the optimal class, not the actual optimal estimator from f-o optimality.
For situations where the radius is (partially) unknown, the concept of a minimax radius has been introduced and determined in Rieder et al. (2008) : A radius r 0 is determined such that the (f-o) maximal inefficiencyρ(r ) (as defined in (5.1)) is minimized in r = r 0 . We translate this to the s-o setup; the s-o results in the Gaussian location model show that neither c 1 (r 1 , ·), nor s-o minimax radius r 1 (·) vary much in n and that for all n, s-o minimax inefficiency is always smaller than the corresponding f-o one.
Asymptotics also helps to understand which contaminations are (already) dangerous: We determine the cniper contamination as a most innocent appearing least favorable contamination, which is shown to form a saddlepoint together with the f-o (s-o) optimal M-estimator. It appears to be innocent, as it produces only "outliers" which are hardest to detect in some sense specified in this section.
Organization of the paper We start with the setup of one dimensional location and recall the main theorem of Ruckdeschel (2010a) in section 2. This result is generalized to a over-/undershooting probability loss in section 3.
Consequences of Theorem 2.1 as to higher order robust optimality are discussed in section 4. As a (partial) explanation for the good, respectively excellent behavior of f-o-o, s-o-o and t-o-o procedures as to numerically exact finite maximal MSE, we present an argument based on a functional implicit function theorem in section 4.2. For decisions upon the procedure to take, only relative risk is relevant which is discussed in some detail in subsection 4.3. Section 5 then considers further supplementary results to Theorem 2.1: a s-o variant of the minimax radius and s-o cniper contaminations. The proofs to the theorems and propositions of this paper are collected in section A.
We consider estimation of parameter θ in a one-dimensional location model, i.e.
for some ideal distribution F with finite Fisher-Information of location I(F), i.e.
We also assume that Λ f is increasing. By translation equivariance, we may restrict ourselves to θ 0 = 0 which will be suppressed in the notation. The set of influence curves (IC's) Ψ for the estimation of θ is defined as Rieder (1994) Ψ
where both expectations are evaluated under F. As class of estimators we consider asymptotically linear estimators (ALE's), i.e. estimators S n = S n (X 1 , . . . , X n ) with the property √ n S n = 1 √ n n i=1 ψ(X i ) + o F n (n 0 ) (2.4)
We consider maximal mean squared error (MSE) on shrinking neighborhoods of this ideal model, defined as the set Q n (r) of distributions L real θ (X 1 , . . . , X n ) = Q n = n i=1
[(1 − r n √ n )F + r n √ n P di n,i ] (2.5) with r n = min(r, √ n), r > 0 the contamination radius and P di n,i ∈ M 1 (B) arbitrary, uncontrollable contaminating distributions. As usual, we interpret Q n as the distribution of the vector (X i ) i≤n with components
Suppressing the dependency upon θ as usual, in Rieder (1994) , the first order expansion of maximal MSE of an ALE is derived as R(S n , r) = r 2 sup |ψ| 2 + E id |ψ| 2 (2.7)
The (first-order) MSE-optimal IC η b 0 in a smooth p-dimensional parametric model with L 2 -derivative Λ by Theorem 5.5.7 (ibid.) has to be of Hampel form
In our location context, for Lagrange multipliers z and A such that η b 0 = η c 0 ∈ Ψ , we get that
In Ruckdeschel (2010a) we obtain corresponding higher order expansions of the maximal MSE if we thin out the neighborhood system to the setQ n (r; ε 0 ) of conditional distributions
where ε 0 = 1/(2 + δ 0 ) is the functional (Huber (1981, (2.39) ,(2.40))) and the finite sample (ε-contamination) breakdown point (Donoho and Huber (1983, section 2. 2)) of the corresponding M-estimator and δ 0 is defined by
For the result we use the following assumptions and notation: To scores function ψ : R → R let ψ t (x) := ψ(x − t) and define the following functions L(t) :
Lety n andŷ n sequences in R such that for some γ > 1, ψ(y n ) = inf ψ + o( 1 n γ ), ψ(ŷ n ) = sup ψ + o( 1 n γ ). For H ∈ M 1 (B n ) and an ordered set of indices I = (1 ≤ i 1 < . . . < i k ≤ n) denote H I the marginal of H with respect to I. Consider three sequences c n , d n , and κ n in R, in (0, ∞), and in {1, . . . , n}, respectively. We say that the sequence (H (n) ) ⊂ M 1 (B n ) is κ n -concentrated left [right] of c n up to o(d n ), if for each sequence of ordered sets I n of cardinality i n ≤ κ n 1 − H (n)
. For the theorem we make the following assumptions:
For some δ ∈ (0, 1], L, V, ρ, and κ as defined above allow the expansions
(2.14)
(Pd) There are some T > 0 and η > 0 such that
With these preparations, we have the following theorem (Ruckdeschel (2010a, Thm. 3 .5))
Theorem 2.1 In our one-dim. location model assume (bmi) to (C) (a) the maximal MSE of the M-estimator S n to scores-function ψ expands to
and we are in the − [+]-case depending on whether (2.20) or (2.21) below applies.
(b) let P di n := n i=1 P di n,i be contaminating measures for (2.5). Then Q n with P di n as contaminating measures generates maximal risk in (2.17) if for k 1 > 1 and k 2 > 2 ∨ ( 3 2 + 3 2δ ) with δ from (Vb) and K 1 (n) = k 1 r √ n either (P di n ) is K 1 (n)-concentrated left ofy n − b k 2 log(n)/n up to o(n −1 ) (2.20)
or (P di n ) is K 1 (n)-concentrated right ofŷ n + b k 2 log(n)/n up to o(n −1 ) 
If sup ψ = − inf ψ and there is "=" in (2.22), (2.20) and (2.21) generate the same risk up to order o(n −1 ).
Special cases Let Q 0 n be any distribution inQ n attaining maximal risk in Theorem 2.1. Under symmetry or more specifically if
we obtain as maximal risk in (2.17) 24) while under r = 0 (with or without (2.23)), we get
respectively, again under (2.23),
(2.26)
One easily shows that under similar condition as for Theorem 2.1, we may replace the squared loss function in the MSE by other loss functions growing atmost at a polynomial rate. In this respect, Theorem 2.1 easily extends to uniform convergence of other risks onQ n , e.g. absolute error ( (x) = |x|), L k -error ( (x) = |x| k ) for 1 < k < ∞, and certain covering probabilities, (x) = I (α 1 ,α 2 ) (x) for some α 1 < α 2 ∈ R.
As an illustration, we consider this last type of loss function, more specifically in the form in which it arises in the finite minimax estimation theory as in Huber (1968) and in which it has been extended to an as. . setup by Rieder (1980) : The risk is defined as Fraiman et al. (2001) have taken up a similar setup with conventional confidence intervals to cover bias and variance simultaneously. We work in the setup of Rieder (1980) here and confine ourselves to the higher order terms of order n −1/2 , but of course an extension to terms up to order n −1 as in Theorem 2.1 is feasible. Due to translation equivariance, it is no restriction to consider the case θ = 0 only. As in Rieder (1980) , we work with a possibly asymmetric partition of the interval of given length 2a/ √ n laid around the estimator: Using the partition
we minimize the risk according to Rieder (1980, formulas (2.8 ) and (2.11) in), if witȟ b,b, andb from (2.12) and
If we now account for terms of order 1 √ n we minimize the risk if we use the partition
δ = δ n given in the theorem below. To this end, let
Then, with Φ and ϕ c.d.f. and density of N(0, 1) and using the notation of Theorem 2.1, we have Theorem 3.1 For the location model (2.1) of finite Fisher information (2.2), assume (bmi), (D') and (C'). Then for sample size n, the minimal over-/undershooting probability of an M-estimator S n for scores-function ψ in Q n obtains eventually in n as
with Q 0 n; − resp. Q 0 n; + according to (2.20) resp. (2.21) and
and δ = δ n according to
Remark 3.2 (a) If l 2 =ṽ 1 = 0 andb = −b, we obtain the same result as (3.8), if we use the expressions b n := Bias n and v 2 n = Var n for bias and variance from Ruckdeschel (2010a, Prop.6.4), plug them into the as. . risk, which gives Φ((rb n − a)/v n ), and then expand this up to o(n −1/2 ).
(b) The numerical values obtainable by Theorem 3.1 should be compared to those of Kohl (2005, sections 11.3.3.3 and 11.4.1); admittedly the approach of Theorem 3.1 in this context gives rather poor (too liberal) approximations compared to those in the cited reference (see the R-file Thm31.R available on the web-page to this article); this is plausible though, as Kohl already starts with finitely optimal procedures whereas our approach improves upon asymptotically optimal ones.
In this section, we consider the class S 2 of all M-estimators according to (bmi), (D'), and (C') as well as (Pd); correspondingly, we define S 3 with (D), (C) replacing (D'), (C'); we always assume that the class of M-estimators H of ICs of Hampel-type (2.9) forms a subset of S 2 [S 3 ]. In particular we assume f to be log-concave.
Symmetry allows considerable simplifications; for instance, if F is symmetric, i.e. F(B) = F(−B) for all B ∈ B, in (2.9) always z = 0. But also, much deeper results are possible. Thus for the rest of this subsection, we assume (2.23). Then (2.24) gives the s-o-maximal MSE for any M-estimator in S 2 ; in particular
Condition (2.23) clearly holds for skew symmetric ψ and symmetric F. For symmetric F, however, for any IC ψ, alsoψ := −ψ(− · ) is an IC and hence so is the skew-symmetrized ψ (s) := 1 2 (ψ +ψ), too. But by convexity of the MSE, ψ (s) will be at least as good as ψ as to MSE, hence it is no restriction to only consider skew symmetric ICs, and we fall into the application range of Ruckdeschel and Rieder (2004, Thm. 3 .1), i.e., Theorem 4.1 Assume that maximal as. . risk of an ALE onQ n resp.Q n ( · , s 0 ) is representable as G(rb(ψ), v 0 (ψ)) for some convex real-valued function G(w, s), strictly isotone in both arguments and totally differentiable, bounded away from the minimum for w → ∞. Then, on Q n , respectively onQ n , the optimal IC of Hampel-type (2.9) for some clipping height b = Ac determined by
In our case, this theorem specializes to 
That is, (for n large enough) the f-o-o clipping height c 0 always is too optimistic. Assume s-o risk of ICs of Hampel-type (2.9) is smooth enough in c in its minimum c 1 to allow a s-o Taylor expansion, which is an assumption on the remainder o(n −1 ) present in (2.17). Then, around c 1 , s-o risk behaves like a parabola. But, as by
using c 1 instead of c 0 can only improve s-o risk by order O(1/n). This even carries over to risks "near" s-o risk:
The drawback of this proposition is that assumption (4.5) is difficult to check if we have no explicit expression for G n : For given r ≥ 0, let asMSE j=0,1,2 (c)be the f-o, s-o, and t-o maximal MSE of an M-estimator in H, and exMSE(c) the corresponding exact maximal MSE R n ; we would like to apply Proposition 4.3 to F = asMSE 0 , F n = asMSE j=1,2 and G n = exMSE to conclude on the performance of f-o-o, s-oo, t-o-o procedures as to exMSE. As to (4.5), part (ii) is easy to see checking the expressions, giving β = 1/2, while for part (i) Theorem 2.1 only says that sup
, and probably, under slightly stronger assumptions, O(n −( j+1)/2 ). So presumably-in view of Table 2 ,
Remark 4.4 We even conjecture that we may apply an analogue to Proposition 4.3 for functions F, F n , G n : Ψ → R: Let us denote byψ ( j;n) , the corresponding f-o, s-o, t-o optimal IC andψ (ex;n) the exactly optimal IC; then, with the usual abuse of notation as to exMSE, we conjecture that 0 ≤ exMSE(ψ ( j;n) ) − exMSE(ψ (ex;n) ) = O(n − j−1 ), j = 0, 1, 2 (4.7)
An observation in the simulation study was that the relative MSE w.r.t. the MSE of the f-o-o procedure seemed to converge faster than the absolute terms. This is reflected by our formulas as follows:
Let asMSE 0 (c) and A 
So in fact, the observed faster convergence is not reflected by higher order optimality, but as we will see, the difference between relMSE 0 (c, r) and relMSE 1 (c, r) are in fact small. Procedure choice will usually be based on relative risk, so it is interesting to consider the maximal error compared to the s-o approximation one incurs when using the f-o asymptotics instead. In view of subsection 4.1 we will limit ourselves to only considering Hampel-IC's with a clipping height c in the range
for ρ ≥ 0. This leads us to
or even maximizing over the radius
In the Gaussian case, the function r → ∆relMSE(r; ρ) is plotted for ρ = 0.1 in Figure 1 , and for ∆(0.1), we get a value of 0.065, which for an actual sample size n has to be divided by √ n-an astonishingly good approximation! So down to very moderate sample sizes we can base our decision which clipping height to take to achieve "nearly" the optimal MSE onQ n on f-o asymptotics only. A similar consideration is of course possible for the ideal situation.
As an example we take F = N(0, 1) and calculate the terms c 1 , asMSE 1 := asMSE 0 + r √ n A 1 (4.13) and relMSE 1 for the radii and sample sizes of the simulation study where for the optimization for c 1 we use the function optimize in R 2.11.0 (compare R Development Core Team (2010)). The results are tabulated in Table 1 . Correspondingly, we also determine the t-o terms c 2 , asMSE 2 := asMSE 1 + A 2 /n (4.14) and in Figure 2 , we plot the graphs of the five functions r → asMSE 0 (η c 0 (r) , r), r → asMSE 1 (η c 0 (r) , r, n), r → asMSE 2 (η c 0 (r) , r, n) r → asMSE 1 (η c 1 (r,n) , r, n), r → asMSE 2 (η c 2 (r,n) , r, n)
for F = N(0, 1) and for n = 30. In fact, the choice of the clipping height-c 0 (r), c 1 (r, n), c 2 (r, n)-does not entail any visible changes while the absolute value of f-o, s-o, and t-o MSE clearly differ.
In the same situation, the three functions r → c 0 (r), r → c 1 (r, n), r → c 2 (r, n) are plotted in Figure 3 ; while there are visible differences between c 0 (r) and c i (r, n), i = 1, 2, c 1 (r, n) and c 2 (r, n) visually coincide. Fraiman et al. (2001) work in a similar setup, i.e. the one-dimensional location problem where the center distribution is F 0 = N(0, σ 2 ) and an M-estimator S n to skew symmetric scores ψ is searched which minimizes the maximal risk on a neighborhood about F 0 . Contrary to our approach, the authors work with convex contamination neighborhoods V = V(F, ε) to a fixed radius ε.
There has been some discussion which approach-fixed or shrinking radius-is more appropriate, but for fixed sample size n, of course we may translate the fixed radius ε into our radius r/ √ n and then compare the approximation quality of both approaches. Fraiman et al. (2001) propose to use risks which are constructed by means of a positive function g : R × R + → R + of as. . bias B = B(F, ψ) and as. . variance v 2 = V 2 (F, ψ).
Here, B is defined as zero of β → (1 − ε) ψ β dF + εb, and v 2 :
Function g is assumed lower semicontinuous and symmetric in the first argument as well as isotone in each argument. The risk of an M-estimator to IC ψ is taken as the function
A MSE-type risk then is given by g(u, v) = u 2 + v. It is not quite MSE, as it employs the as. . terms B and v, so their results may differ from ours. The crucial point is that to solve their optimization problem, the authors have to assume that besides bias, also variance is maximized (for their optimalψ) if we contaminate with a Dirac measure in ∞. According to this assumption, if we introduce G 0 := (1 − ε)F 0 + ε I {∞} , we have to find ψ minimizing
Differently to the Hampel-type IC's the solutions to this problem are of form
but the "MSE"-optimal solutions are numerically quite close to corresponding Hampel-ICs ψ H , for which the authors in turn show that always L g (ψ H ) = l g (ψ H ).
For an implementation of this optimization see the R-file FYZ.R available on the webpage.
As a sort of benchmark for our results, we reproduce a comparison to be found in Ruckdeschel and Kohl (2010) -albeit in some more detail than in the cited reference: For a set of values for n and r, we determine the "MSE"-optimalψ and a corresponding Hampel ICψ H which is then compared to the f-o-o and s-o-o IC derived in this paper. Within the class of Hampel-IC's, numerically, we also determine the t-o-o and the "exactly" optimal clipping-c, c 2 and c ex respectively. We compare the resulting IC's as to their clipping-height and the corresponding (numerically exact) value of R n (S n , r), denoted by MSE n ; the latter comparison is done by the terms relMSE ex n (c · ), calculated as
The results are displayed in Table 2 . Also compare the function allMSEs in the R-file asMSE.R available on the web-page to this article. For the numerical evaluation of the MSE, we use Algorithms C (more accurate, but slow for larger n) and D (a little inaccurate for small n, but fast) discussed in Ruckdeschel and . For n = ∞, we evaluate the corresponding f-o as. . MSE for the IC to the corresponding values of c. As a cross-check, the clipping heights c i , i = 0, 1, 2 are also determined for n = 10 8 . In case of c FZY , for all finite n's the error tolerance used in optimize in R was 10 −4 , while for n = ∞ it was 10 −12 . For c ex and n = 10 8 , an optimization of the (numerically) exact MSE would have been too timeconsuming and has been skipped for this reason. Also, for n = 5, the radius r = 1.0, corresponding to ε = 0.447, is not admitted for an optimization of (4.16) and thus no result is available in this case.
In this subsection, we refine the results of Rieder et al. (2008) . In the cited paper, we want to give a guideline to the statistician which procedure to choose if he knows that there is contamination but does not know the radius exactly: To this end, we consider the maximal inefficiencyρ(r ) defined as ρ 0 (r ) := sup r∈(r l ,r u )ρ (r , r),ρ(r , r) :=R (η c 0 (r ) , r) R(η c 0 (r) , r) (5.1) and determine the minimax radius r 0 as minimizer ofρ 0 (r ). If one knows at least that the actual radius will lie in an interval [r/γ, rγ] we may determine r γ,r as minimizer ofρ γ (r , r) = sup s∈(r/γ,rγ)ρ (r , s) and denote the corresponding minimax inefficiency byρ γ (r). In a second optimizing step we then determine the maximizer r γ ofρ γ (r). The unrestricted case is symbolically included by γ = ∞. In the Gaussian location case this gives γ = 0 γ = 2 γ = 3 r 0 c 0 (r 0 )ρ 0 (r 0 ) r 2 c 0 (r 2 )ρ 2 (r 2 ) r 3 c 0 (r 3 )ρ 3 (r 3 ) 0.621 0.718 18.07% 0.575 0.769 8.84% 0.549 0.799 4.41%
These calculations can easily be translated to the s-o setup setting R 1 (ψ, r, n) := r 2 sup |ψ| 2 + E ψ 2 + r √ n A 1 (5.2) so that in this paper we would instead determine r 1 (n) as minimizer of ρ 1 (r , r, n), sup r∈(r l ,r u ) ρ 1 (r , r, n), ρ 1 (r , r, n) := R 1 (η c 1 (r (n),n) , r, n) R 1 (η c 1 (r,n) , r, n) (5.3) respectively ρ 1;γ and instead ofρ γ . For finite n, however, we have to take into account that r < √ n always. Doing so we get Table 3 , showing that there is not much variation in both c 1 (r ∞ , ·), ρ 1;γ (r γ , ·) for varying n. So if r is completely unknown, it is a good choice to use the M-estimator to Hampel-scores for c ≈ 0.7-you will never have a larger inefficiency than the limiting 18%! Ex post this is one more argument, why the H07-estimate survived in in Sections 7.B.8 and 7.C.4 of the Princeton robustness study (Andrews et al. (1972) ). A table for the corresponding t-o minimax radii is available on the web-page.
In Huber (1997, p. 62) , the author complains ". . . the considerable confusion between the respective roles of diagnostics and robustness. The purpose of robustness is to safeguard against deviations from the assumptions, in particular against those that are near or below the limits of detectability." As worked out in Ruckdeschel (2006) , the exact critical rate for these limits may be determined in a statistical way: For some prescribed outlier set OUT, let p 0 and q n = (1 − r n )p 0 + r n be the probability under the ideal model, and under convex contaminations of radius r n , respectively. Considering the minimax test between these alternatives yields the exact critical rate 1/ √ n: under a faster shrinking p 0 cannot be separated from q n at all, while at a slower rate, asymptotically we can separate them without error.
Going one step further, for some given 1/ √ n-shrinking neighborhoods of radius r, we would also like to know how "small" an outlier may be, while it is still harmful enough to distort the classically optimal procedure in a way that this procedure is beaten by some robust one.
To a fixed radius r, in the preceding sections, we have found/discussed f-o-o and s-oo ICs of Hampel-form with clipping height c j = c j (r[, n]), j = 0, 1. To these ICs we have derived families of contaminations achieving maximal risk onQ n (r). By means of Theorem 2.1(b), these are induced by any contaminating measures P di n under which η θ (X di ) is constantly either b j or −b j for b j = A j c j -up to an event of probability o(n −1 ). Out of these risk-maximizing contaminations, let us limit ourselves to those induced by Dirac masses at x:
Among these Q n (x), we seek the least "suspicious" looking contamination point x in the sense that the region OUT j := [x; ∞) [or (−∞;
x)] carries large ideal probability. With this region as outlier set in Ruckdeschel (2006) , values of x (or slightly above in absolute value) occurring more frequently than they should under the ideal situation, are hardest to detect. More precisely, in the general smooth parametric setup (compare Kohl et al. (2010) ), assume that the observations are univariate; let S (b 0 ) n andŜ n be ALEs to the classical optimal ICη = I −1 Λ and the asMSE 0 -optimal IC η b 0 , respectively. In this setup we define n , Q n (x)) < asMSE 0 (Ŝ n , Q n (x))}
x 0,− := sup{x < 0 asMSE 0 (S (b 0 ) n , Q n (x)) < asMSE 0 (Ŝ n , Q n (x))} (5.5) Remark 5.2 (a) The name cniper point is due to H. Rieder; it alludes to the fact that this "Ianustype" contamination Q n (x 0 ) pretends to be nice, but to the contrary is in fact pernicious, "sniping" off the classically optimal procedure. . .
(b) The cniper concept is of course not bound to quadratic loss. In the obvious manor, the concept may be generalized for multivariate observations, if we define any x 0 of minimal absolute as cniper point.
(c) To get rid of the dependency upon the radius r, in the examples we will use the minimax radii r γ (n) defined in the preceding section.
Correspondingly, in the setup of this paper and under (2.23), let S n , Q n (x)) < asMSE 1 (Ŝ n , Q n (x))}
x 1,− := sup{x < 0 asMSE 1 (S (c 1 ) n , Q n (x)) < asMSE 1 (Ŝ n , Q n (x))} n , Q n (x 0 )) is a saddlepoint for the class of all pairs
where S n are ALE's to IC's of form (2.8) and Q n ∈ Q n w.r.t. f-o riskR. Under (2.23), the same holds in the one-dimensional location model for the pair (S (c 1 ) n , Q n (x 1 )) w.r.t. s-o risk inQ(r).
Remark 5.5 A sufficient condition for (5.7) is that Λ(x) = −Λ(−x): Then for any b > 0, a b = 0 is possible and,
For numerical evaluations, we consider the Gaussian location model and the Gaussian location and scale model. In both models, x j,+ = −x j,− , and without loss, we use x j,+ . For the as. . tests between q n = p 0 and q n > p 0 , alluded to in the beginning of this section, we note that
As to the (f-o) as. . minimax test Ruckdeschel (2006, formula (6. 1)) gives as as. . risk
For s-o asymptotics, we instead use the finite-sample minimax test, i.e. the Neyman-Pearson test with equal Type-I and Type-II error. In our case this is a corresponding randomized binomial test.
In the Gaussian location model, we draw all necessary expressions from Ruckdeschel (2010a, Prop. ) ; in particular, with c 1 = c 1 (n, r γ ), and A 1 = (2Φ(c 1 ) − 1) −1 , b 1 = c 1 A 1 , by Theorem 2.1(b), maximizing risk amounts to either X di > c 1 always or X di < −c 1 always. The classically optimal estimator is the arithmetic mean, and one easily calculates E Q n (x) [x 2 n K = k] = 1 n 2 [k 2 x 2 + (n − k)] (5.11) and integrating out K we get directly
Combining this with formulas (2.17) and (4.1), for M 0 := asMSE 0 (S (c 1 ) n ) we get This yields the results as in Table 4 . We include the type-II error 1 − β(α) for the Neyman Pearson test to niveau α = 5% and the risk ε n of the corresponding minimax test; roughly speaking we cannot do better than overlooking one of 10 contaminations at niveau 5% ideal observations to be falsely marked as outliers, and, equally weighting the two error types we cannot do better than with a false classification rate of 7% for each error type.
To give one more example, consider the one-dimensional location-scale model at central distribution N(0, 1). For this model we have not yet established a s-o as. . theory; for f-o asymptotics, however, we may use R-programs from the bundle RobASt, cf. Kohl (2005, Appendix D) , and get r ∞ = 0.579, max Q n ∈Q n (r ∞ ) asMSE(η θ;0 , Q n ) = 3.123 (5.15) while I −1 θ Λ θ = (x, 1 2 (x 2 − 1)) τ . This gives x 0 = 1.844-and hence ε ∞ = 5.737% and 1 − β ∞ (5%) = 6.557%. Condition (5.7) is proved to hold in subsection A.6.
∼ F, i = 1, . . . , n be real-valued random variables, |ξ i | ≤ 1 Then for µ = E[ξ 1 ] and 0 < ε < 1 − µ Hoeffding (1963) , Thm. 1, inequality (2.1).
To settle case (II) in the proof of Theorem 3.1, we need the following sharpening of Ruckdeschel (2010b, Lem. A.2) Lemma A.2 Let k 1 (n) = 1 + d n and assume that for some δ ∈ (0, 1/4),
Then if lim inf n d n > 0 there is some c > 0 such that
and, if d n = o(n 0 ), for any 0 < δ 0 ≤ 2δ, it holds that
Remark A.3 Even if d n is increasing at a faster rate than n 1/2 , assertion (A.3) remains true, as long as lim inf n d n > 0-but this is not needed here.
Proof Let
Then K n > 0, as log(x) > 0 for x > 1 and By the second assumption in (A.2), d n = o( √ n ), so 0 < d n r/ √ n < 1 − r/ √ n eventually in n and Hoeffding's Lemma A.1 is available; applying it to the case of n independent Bin(1, p) variables, we obtain for B n ∼ Bin(n, p n ), p n = r/ √ n and ε = (k 1 (n) − 1)r/ √ n (which is smaller than 1 − p n eventually)
If lim inf n d n > 0, by (A.5) lim inf n K n > 0, and for any 0 < c < lim inf n K n , (A.3) follows. If d n = o(n 0 ), we note that
which for any δ > 0 entails Pr(Bin(n, r/ √ n ) > k 1 (n)r √ n ) = o exp − rd 2 n √ n 2 + δ Now for d n = o(n 0 ), by the first assumption in (A.2), for 0 < δ 0 < 2δ eventually in n, (A.4) holds as
Another consequence of the exponential decay of (A.3)/(A.4) is that we may neglect values of K > k 1 (n)r √ n when integrating along K.
Corollary A.4 Let K ∼ Bin(n, r/ √ n ). Then, in the setup of Lemma A.2, for any j ∈ N,
for any 0 < d < √ n if lim inf n d n > 0 and any 0 < d ≤ δ 0 if lim n d n = 0.
A.2 Proof of Theorem 3.1
In the risk, we have to treat stochastic arguments in Φ, ϕ; this is settled in the following lemma:
Lemma A.5 Let F : R → R be twice differentiable with Hölder-continuous second derivative and G : R → R be differentiable with Hölder-continuous derivative. Then there is a sequence k 1 (n) = 1+d n with d n → 0 according to (A.2) and some η > 0, such that for all x, β ∈ R and withk = K/ √ n,
and
Proof Using the Taylor approximation of log(1 + x), we get for n sufficiently large
By (A.4) of Lemma A.2, for some δ 0 and eventually in n we have P(K > k 1 (n)r √ n ) ≤ exp(−rn δ 0 ), and by the same argument we also get that P(K < (2 − k 1 (n))r √ n ) ≤ exp(−rn δ 0 ). Hence,
Thus, as F, G are bounded, the contribution of the set {|k − r| > rd n } decays exponentially, while on the complement we have a uniformly bounded Taylor expansion up to order 2 respectively 1 for the integrands:
Integrating these expansions out ink, we see that the first contribution to the Taylor series for F is the quadratic term, which is F (x + βr) β 2 2 Vark, and the remainder is o(n −1/2 ). For G, the first contribution to the error term is the remainder, hence of form const|k − r| 1+η . By the Hölder inequality this gives a bound const [Vark] 1+η 2 = O(n −(1+η)/4 ). For the proof of Theorem 3.1, we use a tableau like the one of Ruckdeschel (2010a, p. 19) , i.e., to derive the result, we partition the integrand according to K < k 1 (n)r √ n k 1 (n)r √ n ≤ K < ε 0 n |t| ≤ k 2 b 2 log(n)/n (I)
with k 1 (n) according to (A.2) . This time, no integration w.r.t. t is needed, so case (IV) from Ruckdeschel (2010a) may be canceled, which is why we may dispense of assumption (Pd) and pass to the unrestricted neighborhoods Q n . Cases (II) and (III) may be taken over unchanged from Ruckdeschel (2010a, Proof of Thm. 3.5), so we may confine us to case (I):
We use α 1 , α 2 from (3.2) and proceed paralleling the proof in Ruckdeschel (2010a) and get from formula (A.18) therein that Pr(S n ≤ − α 1 √ n | D k,t ) =G n ( − α 1 √ n ) + O(n −3/2 ). So we have to spell out s n,k ( −α 1 √ n ), which gives
and hence-settings = s n,k ( −α 1 √ n ) ands 1 = −(α 1 + t )/v 0 as in Ruckdeschel (2010a) Pr
This term is maximized eventually in n, if −t is maximal or, essentially equivalent, all contaminating mass (up to mass o(n −1/2 )) is concentrated left ofy n from Section 2.2, and then t = k b , and after the substitution according tok := k/ √ n, k := k/ √n , this gives withs k = −(α 1 +kb)/v 0 Pr(S n ≤ − α 1 √ n | D k,t=kb ) = Φ(s k ) + ϕ(s k ) 2 √ nv 0 [α 1k − l 2 α 2 1 − 2s k v 0ṽ1 α 1 − v 0 ρ 0 3 (s 2 k − 1) −k 2b ] + o( 1 √ n ) (A.14)
Now, by (3.6), it holds that s 1 = −(α 1 + rb)/v 0 , so that by an application of Lemma A.5, for Q 0 n; − any sequence of measures according to (2.20) A.4 Proof of Proposition 4.3
We apply Rieder (1994, Theorem 1.4 .7) to the derivatives; this theorem says that for η ∈ C 1 (R) with η(θ 0 ) = 0, η (θ 0 ) 0 for some θ 0 ∈ R, there exists an open neighborhood V 0 ⊂ C 1 (R) such that for every open, connected neighborhood V ⊂ V 0 of η there is a unique, continuous map T : V → R with
even more so, T is continuously bounded differentiable on V with derivative at tangent h
Hence there is an open neighborhood V 0;F of F such that for each connected open neighborhood V F ⊂ V 0;F , we get a unique, continuously bounded differentiable map T : V F → R with
But by assumption (4.5) from some n on, F n and G n will lie in V 0;F , and setting x n = T (F n ), by (A.21) F n (x n ) = 0, and |x n − x 0 | = |T (F n ) − T (F)| ≤ |F n (x 0 )|/F (x 0 ) = O(n −β ) which is (b); again by (4.5),
In particular, eventually in n, F n (x n ) > 0 and hence x n is a minimum of F, so (a) is shown. By (4.5), sup x |F − G n | + |F − G n | + |F − G n | = O(n −β ), so (c) follows just as (a). For (d) we note
To show (e), we introduce d n := y n − x n and write 0 ≤ G n (x n ) − G n (y n ) = G n (y n )d n + G n (y n )d 2 n /2 + o(d 2 n ) = ( f 2 + o(n 0 ))d 2 n /2 + o(d 2 n ) = O(n −2β ) (A.22)
A.6 Proof for (5.7) in the Gaussian location scale model
We abbreviate the location and scale parts by indices l and s respectively. By equivariance we may limit ourselves to the case θ = (0, 1) τ . Due to symmetry, A = A(b) from (2.8) is diagonal for all b with elements A l and A s and we may write
The centering z s (b) after the clipping is necessary, as the scale part is not skew symmetric; in the pure scale case (with known θ l ), the corresponding centering z s = z s (b) is antitone in b, because Λ s is monotone in x 2 : It decreases from 0 to [Φ −1 (3/4)] 2 − 1 −0.545 =:ž. In the combined case, we never reach this extremal case due to the additional location part-compare Kohl (2005, Remark 8.2 .1(a)) wherez s =ā sc /ᾱ − 1 −0.530; in any case, z s > −1 always. Hence in particular, for x 0 = 1.844 and b such that .28) and thus in particular,
s (x 0 )| 2 + A 0;l (b)x 2 0 >η s (x 0 ) 2 + I −2 l x 2 0 = |η(x 0 )| 2 (A.29)