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GLOSSARY OF OUTSOURCING TERMS banks and other nancial institutions, and others needing access to an enterprise s data. facilities management The solution by which the customer entrusts to an external services provider the responsibility for operations and software applications and for the management of the associated instrumentalities (hardware, software, applications programming personnel, etc.), while retaining the general oversight and supervision of its information technology. In broader terms, facilities management may apply to other elds, such as applications maintenance, updating, or revision. fraud An intentional deception, for unjust advantage, that causes loss or inconvenience to the party relying on the false or misleading statement. In contract matters, a fraud is the cause of an error bearing on a material part of the contract. gain-sharing A technique for sharing risk and reward on a long-term basis. Euphemistically, gain-sharing is not labeled as risk sharing, so care must be taken to identify what is being shared and why. grid computing See on-demand computing. The concept of grid comes from the electricity industry. The electrical grid is a network of infrastructure components that generate, transmit, and distribute electricity. By analogy, by icking a switch, you can have the outsourced business services ow directly and instantly to your of ce. homesourcing The sourcing of services to telecommuters, whether acting as employees (insourcing) or as independent contractors (outsourcing). indemni cation A method of shifting legal liability from one party to another by contract. 254

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where hi is the ith diagonal element of the hat matrix and W = diag{yi (1 yi )}. Once this replacement is made, the Newton Raphson algorithm of equation (10.9) can proceed in the usual way. Standard errors are also calculated as usual by taking the square roots of the diagonal elements of I( ) 1 . However, Heinze and Schemper (2002) point out that Wald tests based on the standard errors for variables causing separation can be highly inaccurate (as with conventional maximum likelihood). Instead, they recommend chi-square tests based on differences between penalized log likelihoods. The penalized maximum likelihood method has been implemented in a macro for the SAS system (Heinze 1999) and a library for S-PLUS (Ploner 2001). Using the SAS macro, I estimated models for the data in Tables 10.1 and 10.2. For the data with complete separation (Table 10.1), the parameter estimate was 0.936, with a p-value of 0.0032 (based on a penalized likelihood ratio chi-square). For the data with quasi-complete separation (Table 10.2), the parameter estimate was 0.853 with a p-value of 0.0037. These estimates and p-values are similar to those reported in Section 10.6.4 using the exact method with median unbiased estimation.

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