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1.7 PERFORMANCE COMPARISONS In robustness, optimality (i.e., minimaxity) of a given procedure is an important aspect, but it must be regarded as part of a larger picture. In particular, it must be complemented by pe$ormance comparisons-for different sample sizes and underlying situations, and with other procedures. The so-called Princeton robustness study was a first, and exemplary, investigation of this kind, see Andrews et al. (1972). The Princeton study showed up some intrinsic drawbacks of empirical sampling studies. The main one is that they only can give a collection of punctuated spotlights, since each simulation is done for one specific procedure and one specific situation (sample size and distribution). Even worse, the Monte Carlo sampling variability at each such spotlight may exceed the performance differences one is interested in (e.g., between the effects of the underlying distributions), for all practicable Monte Carlo sample sizes. The Princeton study managed to overcome this in part-that is, for suitably structured families of distributions-by Tukey s Monte Carlo Swindle : utilize information available to the person conducting the Monte Carlo simulation, but not to the statistician applying the procedure. This swindle permits one to reduce the differential sampling variability. After the Princeton study, Tukey proposed an even more sophisticated approach based on the idea that any particular sample configuration can occur under any underlying distribution (provided the latter has a strictly positive density), but its probability of occurrence depends on the latter. This is the basis of the so-called configural polysampling method, see Morgenthaler and Tukey (199 1). Another approach to the investigation of the small sample behavior of robust estimates, avoiding empirical sampling altogether, is based on the so-called small sample asymptotics. This will be discussed in 14.

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There is one more thing to consider before you create the sketch. What should you use to create the offset: the actual block edges or the original sketch The answer to this is a Best Practice type issue.

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discussing how to do mathematics. MacLane set out what he thought was a standard position. You decide what area of mathematics you are interested in, you decide exactly what the terms that you are using mean, you know exactly what objects you are dealing with, and off you go. Atiyah disagreed. As MacLane recorded,

In the following sections, we discu,;s the effects of many of these factors and issues individually.

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Remember that you can Ctrl+select individual features, Shift+select a range, or click-and-drag a box in the FeatureManager to select multiple features. Also keep in mind that if you do not select a feature (other than the base feature), then it will not be placed into the part when you insert the library feature. If there were any relations to the omitted feature, they may display as errors or warnings when you place the feature.

Reference signal r(t)

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Parent Group: This is used to define a recursive hierarchy of data in which all parentchild relationships are represented in the data, such as a company organizational chart. Page breack check boxes: Control page breaks and toggle the header and footer visibility for the group. The Filters, Sorting, Visibility, and Data Output tabs contain similar functionality to the respective tabs on the Table and Matrix Properties pages described earlier. Defining options on these tabs for a group affects the behavior of the selected group only. Both the Matrix and Table report items can contain multiple groups. To manage the order of the groups and simplify managing the groups, use the Table and Matrix Properties pages as described earlier in this chapter. The biggest difference between the Matrix and the Table is that the Matrix can contain group definitions for both the columns and the rows, whereas the Table can only contain group definitions for the rows. Once you know how to create and manage group definitions, you are prepared to work with both the Table and the Matrix.

Degree of uncertainty (continued)

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