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The gain in share price of the long position is offset by the loss from the corresponding short position, and vice versa The only time that a merger arbitrage manager does not short shares of the acquirer is when the merger is structured as an all-cash deal True risk arbitrage managers will deal only with mergers that have of cially been announced as opposed to guessing which companies might become takeover candidates at some point in time Hedge fund managers.

9212 Access Control in CORBA In this section, we begin to focus on the details of CORBA security (see Figure 91 again) by investigating the privilege de nition process as it relates to the client and the target object, and as it involves the interactions of the client and the target object In the CORBA security reference model, the access control process must verify the characteristics of a subject s permissions (via privilege attributes) against the target objects that are themselves managed via control attributes (grouped as domains) and operations (grouped as rights) The combination of privilege attributes, control attributes, and domains provides the means to de ne security requirements, and provides the basis for enforcing those requirements by actual clients against target objects, as shown in Figure 91.

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Wu (1988) and then adjusted for unit nonresponse and poststratification in the same manner as the full sample design weights to arrive at the bootstrap survey weights Rust and Rao (1996) provide an overview of the bootstrap and other resampling methods in the context of stratified multistage cluster sampling In Section 203 we extend the estimating function (EF) bootstrap method of Hu and Kalbfleisch (2000) to the case of complex longitudinal survey data and obtain EF bootstrap standard errors that account for within-subject dependence of repeated measurements as well as clustering of subjects and other survey design features Advantages of the EF bootstrap over the direct bootstrap are discussed in Section 203 Rao and Tausi (2004) similarly developed an EF jackknife method for inference from cross-sectional survey data The GEE approach assumes that the marginal means are correctly specified.

It is therefore important to check the adequacy of the mean specification, using goodness-of-fit tests and other diagnostics, before making inferences on model parameters Several global goodness-of-fit tests have been proposed in the literature for the binary response case and a logistic model, assuming simple random sampling of subjects In particular, for the case of no repeated measurements on a sample individual, Hosmer and Lemeshow (1980) proposed a Pearson chi-squared statistic after partitioning the subjects into groups based on the values of the estimated response probabilities under the assumed mean specification (null hypothesis) Horton et al (1999) studied the case of longitudinal data under simple random sampling and proposed a score test after partitioning all the response probabilities under the null hypothesis into groups, assuming working independence for the repeated measurements As noted by Horton et al.

Privilege attributes are associated with the user (client or other software artifact), referred to as the principal, and are used to capture all of the various security permissions for access control, including the identity (eg, user id) of the principal, the role(s) of the principal, the group(s) that the principal belongs to in the organization, the security clearance (eg, secret, classi ed, etc) of the principal, the target objects and operations to which the principal has been granted access, and any other enterprise-wide privileges Clearly, privilege attributes contain the security requirements for each user (principal) Control attributes are associated with each target object, to track the security privileges for an object s perspective While privilege attributes focused on capabilities of individual principals, control attributes track all of the principals (and their access) on a targetobject-by-target-object basis.

(1999), since global (or omnibus) tests may not have high power to detect specific alternatives one should not regard a nonsignificant goodness-of-fit test as clear evidence that the assumed marginal mean specification gives a good fit Data analysts should look for lack of fit in some other ways as well, guided by subject-matter knowledge The above goodness-of-fit tests are generally not applicable to complex survey data because of cross-sectional dependencies among subjects caused by clustering For the case of no repeated measurements, Graubard et al (1997) used the survey weights and the associated estimated response probabilities to partition the subjects into groups, and then developed goodness-of-fit Wald tests based on the weighted sum of responses and the weighted sum of estimated response probabilities in the groups, taking account of the survey design In Section 204, we extend the Horton et al.

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