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Also, including highly correlated variables into the model under these circumstances reduces the standard errors of estimated parameters Finally, Collins et al (2001) have shown that with MNAR missing data mechanisms, where the cause of missingness cannot be included in the missing data model, bias can be substantial However, including highly correlated, substantively irrelevant variables into the model can reduce this bias, often substantially and, as with the other missing data mechanisms, can reduce the standard errors of estimated parameters, without affecting the important parameter estimates In short, it is essential to include variables that, although substantively irrelevant, are highly correlated with the variables containing missing data Because of these recent ndings, users of model-based missing data procedures must make every attempt to include these model-irrelevant variables With some modelbased procedures, such as LTA (Collins et al, 2000), this is simply not possible (at least at present).



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The problem is when a saga cannot commit completely In that case, one or more compensating transactions must be run, usually in the reverse order of the transactions in the saga, undoing the effects of what has been committed to the database Since sagas release partial results to other sagas and transactions, compensating one or more transactions within one saga may necessitate compensating other transactions within other sagas as well Sometimes a compensating transaction for a transaction within a saga may not be possible or may not make sense For example, consider a saga that consists of a test, a treatment, a second test, and second treatment for a patient Suppose the second test indicates a situation that necessitates undoing the effect of the rst treatment Undoing the rst treatment may or may not be possible.





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computing the minimum distance between the time clusters of the test grasp and a set of model grasps. In the second method, a qualitative fuzzy model is developed, with the help of which both a segmentation and a grasp recognition can be achieved. These rst two methods can be seen as evolving fuzzy systems, whereas the third method uses hidden Markov models for grasp recognition. A comparison of the three methods shows that the rst method is the most effective method, followed by the second and third approaches. To improve the PbD process, these methods will be further developed for the recognition and classi cation of operator movements in a robotic environment using more sensory information about the robot workspace and the objects to be handled.

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Thus, for many analysis problems, the use of multiple imputation is clearly preferred However, for FIML-based SEM programs such as Amos, Mx, LISREL 850, and Mplus, it is quite possible to introduce these substantively irrelevant variables into the model in a way that helps deal with the missing data and does not alter the substantive aspects of the original model Models of this sort have been described recently by Graham (in press) The preliminary evidence is that the practice of adding substantively irrelevant variables has no real drawback, other than increased model complexity One of the problems with multiple imputation is that as the number of variables increases, EM and data augmentation require more iterations and more time for each iteration Thus, one practical drawback to adding many extra variables to the model will be that it may take longer to run In extreme cases, it may even be.

As another example, consider trying to compensate a transaction with a saga that bought 1000 shares of a company Although compensating this transaction is possible technically, the results ( nancially, legally, ethically, etc) may be disastrous The kind of recovery we just discussed for a failed transaction within a saga is called backward recovery Backward recovery obviously leads to the potential for cascading compensation, which may take a long time to carry out and in some cases may not be possible Such cases would require restoring the database from a consistent backup and rolling forward good transactions (see 8 on failure and recovery for more details) This second type of recovery is called forward recovery The third alternative for recovery of a failed transaction within a saga uses a mixed approach.

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In this alternative, instead of using a backup as the starting point for the forward recovery, a save point is used The database is rst rolled back to the save point and then is rolled forward by running all the good transactions Sagas can also cause database consistency loss This can happen since transactions within sagas are interleaved When running sagas, we must make sure that the effect of running transactions within sagas creates a serializable schedule Sagas are a form of.

= H (S|T ) + H (X |S, T ) H (X |S, T )

The design in Figure 1.7 enables the researcher to test three null hypotheses: H0: H0: H0:

The topic of creating utilities is so important that I devote an entire chapter to it. 16 discusses how to create custom Excel utilities using VBA.

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